import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
Data = pd.read_csv (r'C:\Users\erdil/Desktop/all_ticks_wide.csv')
Data.head()
| timestamp | AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012-09-17T06:45:00Z | 22.3978 | 5.2084 | 1.7102 | 3.87 | 1.4683 | 1.1356 | 1.0634 | 6.9909 | 2.9948 | ... | 4.2639 | 0.96 | 29.8072 | 1.0382 | 3.8620 | 1.90 | 0.4172 | 2.5438 | 2.2619 | 0.7789 |
| 1 | 2012-09-17T07:00:00Z | 22.3978 | 5.1938 | 1.7066 | 3.86 | 1.4574 | 1.1275 | 1.0634 | 6.9259 | 2.9948 | ... | 4.2521 | 0.96 | 29.7393 | 1.0382 | 3.8529 | 1.90 | 0.4229 | 2.5266 | 2.2462 | 0.7789 |
| 2 | 2012-09-17T07:15:00Z | 22.3978 | 5.2084 | 1.7102 | NaN | 1.4610 | 1.1356 | 1.0679 | 6.9909 | 2.9855 | ... | 4.2521 | 0.97 | 29.6716 | 1.0463 | 3.8436 | 1.91 | 0.4229 | 2.5266 | 2.2566 | 0.7789 |
| 3 | 2012-09-17T07:30:00Z | 22.3978 | 5.1938 | 1.7102 | 3.86 | 1.4537 | 1.1275 | 1.0679 | 6.9584 | 2.9855 | ... | 4.2521 | 0.97 | 29.7393 | 1.0382 | 3.8529 | 1.91 | 0.4286 | 2.5324 | 2.2619 | 0.7860 |
| 4 | 2012-09-17T07:45:00Z | 22.5649 | 5.2084 | 1.7102 | 3.87 | 1.4574 | 1.1356 | 1.0725 | 6.9909 | 2.9760 | ... | 4.2521 | 0.97 | 29.8072 | 1.0382 | 3.8620 | 1.90 | 0.4286 | 2.5324 | 2.2619 | 0.7789 |
5 rows × 61 columns
Data.describe()
| AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ASUZU | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 48131.000000 | 49209.000000 | 48594.000000 | 48171.000000 | 48335.000000 | 46862.000000 | 48165.000000 | 49045.000000 | 48803.000000 | 48433.000000 | ... | 49077.000000 | 45929.000000 | 49143.000000 | 47659.000000 | 49212.000000 | 48781.000000 | 46055.000000 | 49225.000000 | 45528.000000 | 48807.000000 |
| mean | 20.982235 | 6.473105 | 7.127504 | 3.183542 | 2.060859 | 1.365549 | 1.672102 | 15.388088 | 13.432535 | 6.467033 | ... | 5.660680 | 1.737529 | 62.994535 | 1.220452 | 4.735438 | 5.942711 | 2.434249 | 2.566327 | 4.079695 | 1.248124 |
| std | 2.494002 | 0.944955 | 2.710033 | 0.724332 | 0.575943 | 0.167824 | 0.788365 | 4.531459 | 9.624246 | 2.201036 | ... | 0.818598 | 0.867095 | 32.398117 | 0.459532 | 0.977889 | 2.830465 | 2.552377 | 0.422774 | 1.347020 | 0.311330 |
| min | 0.000100 | 0.000100 | 0.000100 | 0.000000 | 0.000100 | 1.025500 | 0.000100 | 0.000100 | 0.000100 | 0.000100 | ... | 0.000100 | 0.650000 | 0.000100 | 0.000100 | 0.000100 | 0.000000 | 0.000100 | 0.000100 | 0.000100 | 0.000100 |
| 25% | 19.160500 | 5.850000 | 5.208800 | 2.670000 | 1.568900 | 1.225100 | 1.047000 | 11.711100 | 4.940300 | 5.074800 | ... | 5.267300 | 1.060000 | 34.549100 | 0.957100 | 4.032200 | 4.020000 | 0.388600 | 2.268200 | 3.006700 | 1.033800 |
| 50% | 20.646500 | 6.305700 | 6.985300 | 2.930000 | 1.937600 | 1.360200 | 1.259700 | 15.010000 | 9.275700 | 5.949600 | ... | 5.746400 | 1.530000 | 49.554200 | 1.050000 | 4.474200 | 6.320000 | 0.965800 | 2.609300 | 4.107800 | 1.250000 |
| 75% | 22.732000 | 6.932500 | 8.720000 | 3.750000 | 2.421400 | 1.500000 | 2.402100 | 19.087700 | 22.756700 | 7.120000 | ... | 6.260000 | 2.130000 | 93.428700 | 1.370800 | 5.246000 | 7.450000 | 4.230000 | 2.874000 | 4.720600 | 1.426500 |
| max | 28.509000 | 9.212400 | 15.118900 | 5.190000 | 3.514300 | 2.190000 | 3.502100 | 26.427800 | 46.761600 | 15.280000 | ... | 7.350000 | 5.920000 | 139.293700 | 2.757800 | 7.581400 | 14.540000 | 10.674800 | 3.958100 | 9.527500 | 2.443000 |
8 rows × 60 columns
Data.tail(20)
| timestamp | AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 49992 | 2019-07-23T09:45:00Z | 20.50 | 7.75 | 9.19 | 2.47 | 3.25 | 1.22 | 2.89 | 20.32 | NaN | ... | 5.65 | 4.28 | 130.4 | 1.05 | 4.86 | 9.98 | 5.31 | 2.77 | 4.26 | NaN |
| 49993 | 2019-07-23T10:00:00Z | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | 130.4 | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 49994 | 2019-07-23T10:45:00Z | 20.46 | 7.76 | 9.18 | 2.47 | 3.25 | 1.21 | 2.89 | 20.30 | NaN | ... | 5.65 | 4.28 | 130.4 | 1.05 | 4.86 | 9.98 | 5.31 | 2.77 | 4.26 | NaN |
| 49995 | 2019-07-23T11:00:00Z | 20.40 | 7.76 | 9.17 | 2.46 | 3.25 | 1.21 | 2.88 | 20.34 | NaN | ... | 5.66 | 4.28 | 130.6 | 1.05 | 4.87 | 9.96 | 5.34 | 2.76 | 4.27 | NaN |
| 49996 | 2019-07-23T11:15:00Z | 20.40 | 7.74 | 9.17 | 2.46 | 3.24 | 1.21 | 2.87 | 20.36 | NaN | ... | 5.65 | 4.41 | 130.8 | 1.05 | 4.86 | 9.99 | 5.32 | 2.76 | 4.27 | NaN |
| 49997 | 2019-07-23T11:30:00Z | 20.40 | 7.72 | 9.16 | 2.47 | 3.24 | 1.21 | 2.86 | 20.32 | NaN | ... | 5.62 | 4.39 | 130.0 | 1.04 | 4.85 | 9.98 | 5.33 | 2.76 | 4.25 | NaN |
| 49998 | 2019-07-23T11:45:00Z | 20.38 | 7.70 | 9.14 | 2.46 | 3.23 | 1.21 | 2.86 | 20.28 | NaN | ... | 5.60 | 4.37 | 130.4 | 1.05 | 4.85 | 9.97 | 5.33 | 2.76 | 4.24 | NaN |
| 49999 | 2019-07-23T12:00:00Z | 20.46 | 7.71 | 9.15 | 2.46 | 3.24 | 1.21 | 2.87 | 20.28 | NaN | ... | 5.60 | 4.39 | 130.2 | 1.05 | 4.86 | 9.97 | 5.32 | 2.75 | 4.24 | NaN |
| 50000 | 2019-07-23T12:15:00Z | 20.42 | 7.71 | 9.18 | 2.45 | 3.23 | 1.20 | 2.86 | 20.28 | NaN | ... | 5.58 | 4.34 | 130.5 | 1.04 | 4.85 | 9.98 | 5.32 | 2.75 | 4.24 | NaN |
| 50001 | 2019-07-23T12:30:00Z | 20.48 | 7.73 | 9.16 | 2.45 | 3.24 | 1.21 | 2.85 | 20.34 | NaN | ... | 5.60 | 4.34 | 131.0 | 1.04 | 4.88 | 9.98 | 5.33 | 2.76 | 4.26 | NaN |
| 50002 | 2019-07-23T12:45:00Z | 20.50 | 7.72 | 9.18 | 2.45 | 3.23 | 1.21 | 2.86 | 20.34 | NaN | ... | 5.58 | 4.34 | 130.7 | 1.04 | 4.88 | 9.98 | 5.33 | 2.75 | 4.26 | NaN |
| 50003 | 2019-07-23T13:00:00Z | 20.44 | 7.73 | 9.15 | 2.45 | 3.24 | 1.21 | 2.86 | 20.24 | NaN | ... | 5.60 | 4.33 | 131.5 | 1.04 | 4.87 | 9.97 | 5.32 | 2.75 | 4.24 | NaN |
| 50004 | 2019-07-23T13:15:00Z | 20.42 | 7.72 | 9.15 | 2.45 | 3.24 | 1.21 | 2.84 | 20.24 | NaN | ... | 5.59 | 4.32 | 131.0 | 1.04 | 4.88 | 9.97 | 5.34 | 2.75 | 4.25 | NaN |
| 50005 | 2019-07-23T13:30:00Z | 20.46 | 7.73 | 9.14 | 2.47 | 3.24 | 1.21 | 2.84 | 20.18 | NaN | ... | 5.59 | 4.34 | 131.5 | 1.05 | 4.90 | 9.97 | 5.34 | 2.74 | 4.25 | NaN |
| 50006 | 2019-07-23T13:45:00Z | 20.50 | 7.73 | 9.14 | 2.46 | 3.23 | 1.21 | 2.84 | 20.22 | NaN | ... | 5.57 | 4.34 | 131.5 | 1.05 | 4.89 | 9.97 | 5.33 | 2.74 | 4.24 | NaN |
| 50007 | 2019-07-23T14:00:00Z | 20.48 | 7.73 | 9.14 | 2.47 | 3.23 | 1.21 | 2.84 | 20.30 | NaN | ... | 5.60 | 4.34 | 131.6 | 1.05 | 4.86 | 9.98 | 5.35 | 2.75 | 4.25 | NaN |
| 50008 | 2019-07-23T14:15:00Z | 20.50 | 7.72 | 9.14 | 2.47 | 3.22 | 1.21 | 2.84 | 20.32 | NaN | ... | 5.57 | 4.35 | 131.5 | 1.05 | 4.86 | 9.98 | 5.34 | 2.75 | 4.24 | NaN |
| 50009 | 2019-07-23T14:30:00Z | 20.50 | 7.74 | 9.13 | 2.46 | 3.23 | 1.21 | 2.83 | 20.34 | NaN | ... | 5.57 | 4.36 | 131.5 | 1.05 | 4.86 | 9.96 | 5.34 | 2.76 | 4.24 | NaN |
| 50010 | 2019-07-23T14:45:00Z | 20.40 | 7.70 | 9.14 | 2.47 | 3.24 | 1.21 | 2.82 | 20.38 | NaN | ... | 5.57 | 4.35 | 131.3 | 1.04 | 4.86 | 9.94 | 5.34 | 2.77 | 4.24 | NaN |
| 50011 | 2019-07-23T15:00:00Z | 20.46 | 7.70 | 9.14 | 2.47 | 3.23 | 1.20 | 2.83 | 20.32 | NaN | ... | 5.56 | 4.34 | 131.8 | 1.05 | 4.85 | 9.93 | 5.33 | 2.77 | 4.24 | NaN |
20 rows × 61 columns
print(Data.head(20))
timestamp AEFES AKBNK AKSA AKSEN ALARK ALBRK \
0 2012-09-17T06:45:00Z 22.3978 5.2084 1.7102 3.87 1.4683 1.1356
1 2012-09-17T07:00:00Z 22.3978 5.1938 1.7066 3.86 1.4574 1.1275
2 2012-09-17T07:15:00Z 22.3978 5.2084 1.7102 NaN 1.4610 1.1356
3 2012-09-17T07:30:00Z 22.3978 5.1938 1.7102 3.86 1.4537 1.1275
4 2012-09-17T07:45:00Z 22.5649 5.2084 1.7102 3.87 1.4574 1.1356
5 2012-09-17T08:00:00Z 22.5649 5.2229 1.7102 3.86 1.4610 1.1275
6 2012-09-17T08:15:00Z 22.5649 5.2229 1.7066 NaN 1.4610 1.1275
7 2012-09-17T08:30:00Z 22.5649 5.2084 1.7066 3.86 1.4610 NaN
8 2012-09-17T08:45:00Z 22.5649 5.2372 1.6995 NaN 1.4610 1.1275
9 2012-09-17T09:00:00Z 22.5649 5.2372 1.6995 3.86 1.4610 1.1356
10 2012-09-17T09:15:00Z 22.5649 5.2372 1.6995 NaN 1.4574 1.1435
11 2012-09-17T11:00:00Z 22.4815 5.2372 1.6956 NaN 1.4574 1.1435
12 2012-09-17T11:15:00Z 22.4815 5.2084 1.6956 NaN 1.4610 1.1435
13 2012-09-17T11:30:00Z 22.6485 5.2084 1.6920 3.86 1.4574 1.1356
14 2012-09-17T11:45:00Z 22.6485 5.1938 1.6956 3.86 1.4574 1.1435
15 2012-09-17T12:00:00Z 22.4815 5.1793 1.6883 3.86 1.4574 1.1356
16 2012-09-17T12:15:00Z 22.5649 5.1793 1.6956 3.87 1.4574 1.1196
17 2012-09-17T12:30:00Z 22.3141 5.1505 1.6920 3.87 1.4574 1.1275
18 2012-09-17T12:45:00Z 22.3978 5.1793 1.6920 3.87 1.4610 1.1196
19 2012-09-17T13:00:00Z 22.3978 5.1793 1.6956 3.88 1.4537 1.1196
ANACM ARCLK ASELS ... TTKOM TUKAS TUPRS USAK VAKBN \
0 1.0634 6.9909 2.9948 ... 4.2639 0.96 29.8072 1.0382 3.8620
1 1.0634 6.9259 2.9948 ... 4.2521 0.96 29.7393 1.0382 3.8529
2 1.0679 6.9909 2.9855 ... 4.2521 0.97 29.6716 1.0463 3.8436
3 1.0679 6.9584 2.9855 ... 4.2521 0.97 29.7393 1.0382 3.8529
4 1.0725 6.9909 2.9760 ... 4.2521 0.97 29.8072 1.0382 3.8620
5 1.0725 6.9584 2.9760 ... 4.2402 NaN 29.8072 1.0382 3.8620
6 1.0679 6.9584 2.9760 ... 4.2402 0.97 29.6716 1.0382 3.8529
7 1.0725 6.9909 2.9855 ... 4.2168 0.97 29.7393 1.0463 3.8529
8 1.0725 6.9909 2.9855 ... 4.2285 0.96 29.7393 1.0382 3.8620
9 1.0725 6.9909 2.9855 ... 4.2285 0.97 29.7393 1.0382 3.8620
10 1.0725 6.9909 2.9760 ... 4.2402 0.98 29.8072 1.0382 3.8529
11 1.0725 6.9259 2.9855 ... 4.2285 NaN 29.8072 1.0463 3.8529
12 NaN 6.8933 2.9760 ... 4.2285 0.97 29.8072 1.0382 3.8529
13 1.0679 6.8933 2.9855 ... 4.2285 0.97 29.7393 1.0382 3.8529
14 NaN 6.8933 2.9760 ... 4.2285 0.97 29.8748 1.0382 3.8529
15 1.0679 6.8283 2.9855 ... 4.2285 NaN 29.8748 1.0301 3.8529
16 1.0679 6.7958 2.9855 ... 4.2168 0.97 29.8748 1.0382 3.8529
17 1.0679 6.7634 2.9855 ... 4.2168 0.96 29.8072 1.0382 3.8345
18 1.0679 6.8283 2.9760 ... 4.2168 NaN 29.8748 1.0301 3.8436
19 1.0725 6.8283 2.9948 ... 4.2049 0.97 30.0103 1.0382 3.8529
VESTL YATAS YKBNK YUNSA ZOREN
0 1.90 0.4172 2.5438 2.2619 0.7789
1 1.90 0.4229 2.5266 2.2462 0.7789
2 1.91 0.4229 2.5266 2.2566 0.7789
3 1.91 0.4286 2.5324 2.2619 0.7860
4 1.90 0.4286 2.5324 2.2619 0.7789
5 1.91 0.4314 2.5381 2.2566 0.7860
6 1.91 0.4286 2.5324 2.2566 0.7789
7 1.91 0.4286 2.5266 2.2566 0.7860
8 1.90 0.4314 2.5381 2.2514 0.7789
9 1.91 0.4314 2.5324 2.2619 0.7789
10 1.91 0.4314 2.5266 2.2619 0.7789
11 1.91 0.4286 2.5266 NaN 0.7789
12 1.91 0.4286 2.5266 2.2619 0.7789
13 1.91 0.4314 2.5324 2.2671 0.7719
14 1.90 0.4314 2.5324 2.2619 0.7860
15 1.91 0.4286 2.5266 2.2619 0.7789
16 1.90 0.4343 2.5266 2.2619 0.7789
17 1.90 0.4343 2.5208 NaN 0.7789
18 1.90 0.4314 2.5266 2.2619 0.7789
19 1.90 0.4314 2.5324 NaN 0.7719
[20 rows x 61 columns]
Data.shape
(50012, 61)
Data.columns
Index(['timestamp', 'AEFES', 'AKBNK', 'AKSA', 'AKSEN', 'ALARK', 'ALBRK',
'ANACM', 'ARCLK', 'ASELS', 'ASUZU', 'AYGAZ', 'BAGFS', 'BANVT', 'BRISA',
'CCOLA', 'CEMAS', 'ECILC', 'EREGL', 'FROTO', 'GARAN', 'GOODY', 'GUBRF',
'HALKB', 'ICBCT', 'ISCTR', 'ISDMR', 'ISFIN', 'ISYAT', 'KAREL', 'KARSN',
'KCHOL', 'KRDMB', 'KRDMD', 'MGROS', 'OTKAR', 'PARSN', 'PETKM', 'PGSUS',
'PRKME', 'SAHOL', 'SASA', 'SISE', 'SKBNK', 'SODA', 'TCELL', 'THYAO',
'TKFEN', 'TOASO', 'TRKCM', 'TSKB', 'TTKOM', 'TUKAS', 'TUPRS', 'USAK',
'VAKBN', 'VESTL', 'YATAS', 'YKBNK', 'YUNSA', 'ZOREN'],
dtype='object')
Data_fill = Data.ffill()
Data_fill
| timestamp | AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012-09-17T06:45:00Z | 22.3978 | 5.2084 | 1.7102 | 3.87 | 1.4683 | 1.1356 | 1.0634 | 6.9909 | 2.9948 | ... | 4.2639 | 0.96 | 29.8072 | 1.0382 | 3.8620 | 1.90 | 0.4172 | 2.5438 | 2.2619 | 0.7789 |
| 1 | 2012-09-17T07:00:00Z | 22.3978 | 5.1938 | 1.7066 | 3.86 | 1.4574 | 1.1275 | 1.0634 | 6.9259 | 2.9948 | ... | 4.2521 | 0.96 | 29.7393 | 1.0382 | 3.8529 | 1.90 | 0.4229 | 2.5266 | 2.2462 | 0.7789 |
| 2 | 2012-09-17T07:15:00Z | 22.3978 | 5.2084 | 1.7102 | 3.86 | 1.4610 | 1.1356 | 1.0679 | 6.9909 | 2.9855 | ... | 4.2521 | 0.97 | 29.6716 | 1.0463 | 3.8436 | 1.91 | 0.4229 | 2.5266 | 2.2566 | 0.7789 |
| 3 | 2012-09-17T07:30:00Z | 22.3978 | 5.1938 | 1.7102 | 3.86 | 1.4537 | 1.1275 | 1.0679 | 6.9584 | 2.9855 | ... | 4.2521 | 0.97 | 29.7393 | 1.0382 | 3.8529 | 1.91 | 0.4286 | 2.5324 | 2.2619 | 0.7860 |
| 4 | 2012-09-17T07:45:00Z | 22.5649 | 5.2084 | 1.7102 | 3.87 | 1.4574 | 1.1356 | 1.0725 | 6.9909 | 2.9760 | ... | 4.2521 | 0.97 | 29.8072 | 1.0382 | 3.8620 | 1.90 | 0.4286 | 2.5324 | 2.2619 | 0.7789 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 50007 | 2019-07-23T14:00:00Z | 20.4800 | 7.7300 | 9.1400 | 2.47 | 3.2300 | 1.2100 | 2.8400 | 20.3000 | 18.1500 | ... | 5.6000 | 4.34 | 131.6000 | 1.0500 | 4.8600 | 9.98 | 5.3500 | 2.7500 | 4.2500 | 1.1700 |
| 50008 | 2019-07-23T14:15:00Z | 20.5000 | 7.7200 | 9.1400 | 2.47 | 3.2200 | 1.2100 | 2.8400 | 20.3200 | 18.1500 | ... | 5.5700 | 4.35 | 131.5000 | 1.0500 | 4.8600 | 9.98 | 5.3400 | 2.7500 | 4.2400 | 1.1700 |
| 50009 | 2019-07-23T14:30:00Z | 20.5000 | 7.7400 | 9.1300 | 2.46 | 3.2300 | 1.2100 | 2.8300 | 20.3400 | 18.1500 | ... | 5.5700 | 4.36 | 131.5000 | 1.0500 | 4.8600 | 9.96 | 5.3400 | 2.7600 | 4.2400 | 1.1700 |
| 50010 | 2019-07-23T14:45:00Z | 20.4000 | 7.7000 | 9.1400 | 2.47 | 3.2400 | 1.2100 | 2.8200 | 20.3800 | 18.1500 | ... | 5.5700 | 4.35 | 131.3000 | 1.0400 | 4.8600 | 9.94 | 5.3400 | 2.7700 | 4.2400 | 1.1700 |
| 50011 | 2019-07-23T15:00:00Z | 20.4600 | 7.7000 | 9.1400 | 2.47 | 3.2300 | 1.2000 | 2.8300 | 20.3200 | 18.1500 | ... | 5.5600 | 4.34 | 131.8000 | 1.0500 | 4.8500 | 9.93 | 5.3300 | 2.7700 | 4.2400 | 1.1700 |
50012 rows × 61 columns
data = pd.DataFrame(Data_fill)
# Check for null values in the DataFrame
null_data = data.isnull().sum()
# Print out the columns with null values
print("Columns with Null Values:")
print(null_data[null_data > 0])
Columns with Null Values: ISDMR 23955 PGSUS 3997 dtype: int64
Data_filled = Data_fill.bfill()
Data_filled
Data_filled_timeless = Data_filled.drop(columns=['timestamp'])
Data_filled_timeless
| AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ASUZU | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 22.3978 | 5.2084 | 1.7102 | 3.87 | 1.4683 | 1.1356 | 1.0634 | 6.9909 | 2.9948 | 2.4998 | ... | 4.2639 | 0.96 | 29.8072 | 1.0382 | 3.8620 | 1.90 | 0.4172 | 2.5438 | 2.2619 | 0.7789 |
| 1 | 22.3978 | 5.1938 | 1.7066 | 3.86 | 1.4574 | 1.1275 | 1.0634 | 6.9259 | 2.9948 | 2.5100 | ... | 4.2521 | 0.96 | 29.7393 | 1.0382 | 3.8529 | 1.90 | 0.4229 | 2.5266 | 2.2462 | 0.7789 |
| 2 | 22.3978 | 5.2084 | 1.7102 | 3.86 | 1.4610 | 1.1356 | 1.0679 | 6.9909 | 2.9855 | 2.4796 | ... | 4.2521 | 0.97 | 29.6716 | 1.0463 | 3.8436 | 1.91 | 0.4229 | 2.5266 | 2.2566 | 0.7789 |
| 3 | 22.3978 | 5.1938 | 1.7102 | 3.86 | 1.4537 | 1.1275 | 1.0679 | 6.9584 | 2.9855 | 2.4897 | ... | 4.2521 | 0.97 | 29.7393 | 1.0382 | 3.8529 | 1.91 | 0.4286 | 2.5324 | 2.2619 | 0.7860 |
| 4 | 22.5649 | 5.2084 | 1.7102 | 3.87 | 1.4574 | 1.1356 | 1.0725 | 6.9909 | 2.9760 | 2.4897 | ... | 4.2521 | 0.97 | 29.8072 | 1.0382 | 3.8620 | 1.90 | 0.4286 | 2.5324 | 2.2619 | 0.7789 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 50007 | 20.4800 | 7.7300 | 9.1400 | 2.47 | 3.2300 | 1.2100 | 2.8400 | 20.3000 | 18.1500 | 8.1300 | ... | 5.6000 | 4.34 | 131.6000 | 1.0500 | 4.8600 | 9.98 | 5.3500 | 2.7500 | 4.2500 | 1.1700 |
| 50008 | 20.5000 | 7.7200 | 9.1400 | 2.47 | 3.2200 | 1.2100 | 2.8400 | 20.3200 | 18.1500 | 8.0400 | ... | 5.5700 | 4.35 | 131.5000 | 1.0500 | 4.8600 | 9.98 | 5.3400 | 2.7500 | 4.2400 | 1.1700 |
| 50009 | 20.5000 | 7.7400 | 9.1300 | 2.46 | 3.2300 | 1.2100 | 2.8300 | 20.3400 | 18.1500 | 8.0900 | ... | 5.5700 | 4.36 | 131.5000 | 1.0500 | 4.8600 | 9.96 | 5.3400 | 2.7600 | 4.2400 | 1.1700 |
| 50010 | 20.4000 | 7.7000 | 9.1400 | 2.47 | 3.2400 | 1.2100 | 2.8200 | 20.3800 | 18.1500 | 7.9800 | ... | 5.5700 | 4.35 | 131.3000 | 1.0400 | 4.8600 | 9.94 | 5.3400 | 2.7700 | 4.2400 | 1.1700 |
| 50011 | 20.4600 | 7.7000 | 9.1400 | 2.47 | 3.2300 | 1.2000 | 2.8300 | 20.3200 | 18.1500 | 7.9700 | ... | 5.5600 | 4.34 | 131.8000 | 1.0500 | 4.8500 | 9.93 | 5.3300 | 2.7700 | 4.2400 | 1.1700 |
50012 rows × 60 columns
data = pd.DataFrame(Data_filled)
# Check for null values in the DataFrame
null_data = data.isnull().sum()
# Print out the columns with null values
print("Columns with Null Values:")
print(null_data[null_data > 0])
Columns with Null Values: Series([], dtype: int64)
As we can see we filled all our data that were empthy first with forward fill then there were lack of data as well we did backward fill
Data_filled['timestamp'] = pd.to_datetime(Data_filled['timestamp'], format="%Y-%m-%dT%H:%M:%SZ")
print(Data_filled)
timestamp AEFES AKBNK AKSA AKSEN ALARK ALBRK \
0 2012-09-17 06:45:00 22.3978 5.2084 1.7102 3.87 1.4683 1.1356
1 2012-09-17 07:00:00 22.3978 5.1938 1.7066 3.86 1.4574 1.1275
2 2012-09-17 07:15:00 22.3978 5.2084 1.7102 3.86 1.4610 1.1356
3 2012-09-17 07:30:00 22.3978 5.1938 1.7102 3.86 1.4537 1.1275
4 2012-09-17 07:45:00 22.5649 5.2084 1.7102 3.87 1.4574 1.1356
... ... ... ... ... ... ... ...
50007 2019-07-23 14:00:00 20.4800 7.7300 9.1400 2.47 3.2300 1.2100
50008 2019-07-23 14:15:00 20.5000 7.7200 9.1400 2.47 3.2200 1.2100
50009 2019-07-23 14:30:00 20.5000 7.7400 9.1300 2.46 3.2300 1.2100
50010 2019-07-23 14:45:00 20.4000 7.7000 9.1400 2.47 3.2400 1.2100
50011 2019-07-23 15:00:00 20.4600 7.7000 9.1400 2.47 3.2300 1.2000
ANACM ARCLK ASELS ... TTKOM TUKAS TUPRS USAK VAKBN \
0 1.0634 6.9909 2.9948 ... 4.2639 0.96 29.8072 1.0382 3.8620
1 1.0634 6.9259 2.9948 ... 4.2521 0.96 29.7393 1.0382 3.8529
2 1.0679 6.9909 2.9855 ... 4.2521 0.97 29.6716 1.0463 3.8436
3 1.0679 6.9584 2.9855 ... 4.2521 0.97 29.7393 1.0382 3.8529
4 1.0725 6.9909 2.9760 ... 4.2521 0.97 29.8072 1.0382 3.8620
... ... ... ... ... ... ... ... ... ...
50007 2.8400 20.3000 18.1500 ... 5.6000 4.34 131.6000 1.0500 4.8600
50008 2.8400 20.3200 18.1500 ... 5.5700 4.35 131.5000 1.0500 4.8600
50009 2.8300 20.3400 18.1500 ... 5.5700 4.36 131.5000 1.0500 4.8600
50010 2.8200 20.3800 18.1500 ... 5.5700 4.35 131.3000 1.0400 4.8600
50011 2.8300 20.3200 18.1500 ... 5.5600 4.34 131.8000 1.0500 4.8500
VESTL YATAS YKBNK YUNSA ZOREN
0 1.90 0.4172 2.5438 2.2619 0.7789
1 1.90 0.4229 2.5266 2.2462 0.7789
2 1.91 0.4229 2.5266 2.2566 0.7789
3 1.91 0.4286 2.5324 2.2619 0.7860
4 1.90 0.4286 2.5324 2.2619 0.7789
... ... ... ... ... ...
50007 9.98 5.3500 2.7500 4.2500 1.1700
50008 9.98 5.3400 2.7500 4.2400 1.1700
50009 9.96 5.3400 2.7600 4.2400 1.1700
50010 9.94 5.3400 2.7700 4.2400 1.1700
50011 9.93 5.3300 2.7700 4.2400 1.1700
[50012 rows x 61 columns]
# Set the 'timestamp' column as the index
Data_filled.set_index('timestamp', inplace=True)
# Resample the DataFrame by month and apply an aggregation function (e.g., mean)
Data_aggregated = Data_filled.resample('M').mean()
# Reset the index to make 'timestamp' a column again
Data_aggregated.reset_index(inplace=True)
# Display the aggregated DataFrame
Data_aggregated
| timestamp | AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012-09-30 | 21.990191 | 5.123117 | 1.682904 | 3.866231 | 1.419293 | 1.127155 | 1.091068 | 6.575133 | 2.997942 | ... | 4.191370 | 0.954192 | 28.731524 | 1.046830 | 3.710068 | 1.899769 | 0.413282 | 2.498152 | 2.320318 | 0.766005 |
| 1 | 2012-10-31 | 22.436759 | 5.789223 | 1.694708 | 3.698123 | 1.448755 | 1.106620 | 1.150508 | 6.661405 | 3.165903 | ... | 5.483872 | 0.922549 | 28.362370 | 1.197415 | 3.709942 | 1.822332 | 0.344528 | 2.573235 | 2.554721 | 0.741018 |
| 2 | 2012-11-30 | 21.613061 | 6.053151 | 1.730948 | 3.574476 | 1.487183 | 1.202658 | 1.209599 | 7.061526 | 3.155719 | ... | 5.201549 | 0.904545 | 30.145567 | 1.132350 | 3.984920 | 1.786031 | 0.337622 | 2.670650 | 2.765547 | 0.735518 |
| 3 | 2012-12-31 | 21.535772 | 6.349386 | 1.836533 | 4.074751 | 1.667173 | 1.370815 | 1.241861 | 7.238860 | 3.610114 | ... | 5.372824 | 0.930110 | 33.895264 | 1.127946 | 4.297170 | 1.868803 | 0.340183 | 2.966750 | 2.708904 | 0.773274 |
| 4 | 2013-01-31 | 22.277677 | 6.776285 | 1.959165 | 4.631329 | 1.942940 | 1.535015 | 1.358492 | 7.907567 | 3.921994 | ... | 5.784868 | 0.953549 | 34.748862 | 1.146736 | 4.914509 | 2.025717 | 0.343421 | 3.194934 | 2.621166 | 1.008381 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 78 | 2019-03-31 | 19.600504 | 6.507827 | 8.201133 | 2.787143 | 2.653300 | 1.688616 | 3.023953 | 19.270402 | 22.756085 | ... | 5.066548 | 4.134747 | 131.386789 | 1.094851 | 5.148720 | 12.073155 | 5.450997 | 2.168452 | 4.942154 | 1.410863 |
| 79 | 2019-04-30 | 18.934514 | 6.263428 | 8.581837 | 2.438346 | 2.493987 | 1.505261 | 2.933529 | 18.108465 | 21.095927 | ... | 4.406379 | 3.959702 | 129.464978 | 1.023115 | 4.319568 | 12.994531 | 4.849076 | 2.147139 | 4.430529 | 1.276274 |
| 80 | 2019-05-31 | 17.964252 | 5.769545 | 8.199104 | 2.238094 | 2.717440 | 1.243186 | 2.742019 | 16.493457 | 18.234517 | ... | 4.225021 | 3.655647 | 120.396017 | 0.990413 | 3.675861 | 9.969360 | 4.025533 | 1.947127 | 3.965989 | 1.156245 |
| 81 | 2019-06-30 | 19.361205 | 6.487419 | 9.108642 | 2.326004 | 3.151472 | 1.146711 | 2.800497 | 18.380019 | 18.156903 | ... | 4.805430 | 5.031530 | 119.014340 | 1.027648 | 3.876272 | 11.013518 | 4.427476 | 2.243652 | 4.295201 | 1.170459 |
| 82 | 2019-07-31 | 20.240195 | 7.410547 | 8.985977 | 2.393125 | 3.173809 | 1.196523 | 2.822285 | 19.732930 | 17.930801 | ... | 5.328398 | 4.403008 | 121.140430 | 1.042051 | 4.734668 | 10.519551 | 4.880820 | 2.605449 | 4.310781 | 1.195703 |
83 rows × 61 columns
Data_aggregated['year_month'] = Data_aggregated['timestamp'].dt.strftime('%Y-%m')
Data_aggregated = Data_aggregated.drop(columns=['timestamp'])
# Display the DataFrame with the new 'year_month' and 'date' columns
print(Data_aggregated)
AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM \
0 21.990191 5.123117 1.682904 3.866231 1.419293 1.127155 1.091068
1 22.436759 5.789223 1.694708 3.698123 1.448755 1.106620 1.150508
2 21.613061 6.053151 1.730948 3.574476 1.487183 1.202658 1.209599
3 21.535772 6.349386 1.836533 4.074751 1.667173 1.370815 1.241861
4 22.277677 6.776285 1.959165 4.631329 1.942940 1.535015 1.358492
.. ... ... ... ... ... ... ...
78 19.600504 6.507827 8.201133 2.787143 2.653300 1.688616 3.023953
79 18.934514 6.263428 8.581837 2.438346 2.493987 1.505261 2.933529
80 17.964252 5.769545 8.199104 2.238094 2.717440 1.243186 2.742019
81 19.361205 6.487419 9.108642 2.326004 3.151472 1.146711 2.800497
82 20.240195 7.410547 8.985977 2.393125 3.173809 1.196523 2.822285
ARCLK ASELS ASUZU ... TUKAS TUPRS USAK \
0 6.575133 2.997942 2.446388 ... 0.954192 28.731524 1.046830
1 6.661405 3.165903 3.254486 ... 0.922549 28.362370 1.197415
2 7.061526 3.155719 3.982953 ... 0.904545 30.145567 1.132350
3 7.238860 3.610114 4.233941 ... 0.930110 33.895264 1.127946
4 7.907567 3.921994 4.286279 ... 0.953549 34.748862 1.146736
.. ... ... ... ... ... ... ...
78 19.270402 22.756085 7.825997 ... 4.134747 131.386789 1.094851
79 18.108465 21.095927 7.528018 ... 3.959702 129.464978 1.023115
80 16.493457 18.234517 7.159844 ... 3.655647 120.396017 0.990413
81 18.380019 18.156903 6.947476 ... 5.031530 119.014340 1.027648
82 19.732930 17.930801 7.255117 ... 4.403008 121.140430 1.042051
VAKBN VESTL YATAS YKBNK YUNSA ZOREN year_month
0 3.710068 1.899769 0.413282 2.498152 2.320318 0.766005 2012-09
1 3.709942 1.822332 0.344528 2.573235 2.554721 0.741018 2012-10
2 3.984920 1.786031 0.337622 2.670650 2.765547 0.735518 2012-11
3 4.297170 1.868803 0.340183 2.966750 2.708904 0.773274 2012-12
4 4.914509 2.025717 0.343421 3.194934 2.621166 1.008381 2013-01
.. ... ... ... ... ... ... ...
78 5.148720 12.073155 5.450997 2.168452 4.942154 1.410863 2019-03
79 4.319568 12.994531 4.849076 2.147139 4.430529 1.276274 2019-04
80 3.675861 9.969360 4.025533 1.947127 3.965989 1.156245 2019-05
81 3.876272 11.013518 4.427476 2.243652 4.295201 1.170459 2019-06
82 4.734668 10.519551 4.880820 2.605449 4.310781 1.195703 2019-07
[83 rows x 61 columns]
Data_aggregated = Data_aggregated[['year_month'] + [col for col in Data_aggregated if col != 'year_month']]
# Display the modified DataFrame
print(Data_aggregated)
year_month AEFES AKBNK AKSA AKSEN ALARK ALBRK \
0 2012-09 21.990191 5.123117 1.682904 3.866231 1.419293 1.127155
1 2012-10 22.436759 5.789223 1.694708 3.698123 1.448755 1.106620
2 2012-11 21.613061 6.053151 1.730948 3.574476 1.487183 1.202658
3 2012-12 21.535772 6.349386 1.836533 4.074751 1.667173 1.370815
4 2013-01 22.277677 6.776285 1.959165 4.631329 1.942940 1.535015
.. ... ... ... ... ... ... ...
78 2019-03 19.600504 6.507827 8.201133 2.787143 2.653300 1.688616
79 2019-04 18.934514 6.263428 8.581837 2.438346 2.493987 1.505261
80 2019-05 17.964252 5.769545 8.199104 2.238094 2.717440 1.243186
81 2019-06 19.361205 6.487419 9.108642 2.326004 3.151472 1.146711
82 2019-07 20.240195 7.410547 8.985977 2.393125 3.173809 1.196523
ANACM ARCLK ASELS ... TTKOM TUKAS TUPRS \
0 1.091068 6.575133 2.997942 ... 4.191370 0.954192 28.731524
1 1.150508 6.661405 3.165903 ... 5.483872 0.922549 28.362370
2 1.209599 7.061526 3.155719 ... 5.201549 0.904545 30.145567
3 1.241861 7.238860 3.610114 ... 5.372824 0.930110 33.895264
4 1.358492 7.907567 3.921994 ... 5.784868 0.953549 34.748862
.. ... ... ... ... ... ... ...
78 3.023953 19.270402 22.756085 ... 5.066548 4.134747 131.386789
79 2.933529 18.108465 21.095927 ... 4.406379 3.959702 129.464978
80 2.742019 16.493457 18.234517 ... 4.225021 3.655647 120.396017
81 2.800497 18.380019 18.156903 ... 4.805430 5.031530 119.014340
82 2.822285 19.732930 17.930801 ... 5.328398 4.403008 121.140430
USAK VAKBN VESTL YATAS YKBNK YUNSA ZOREN
0 1.046830 3.710068 1.899769 0.413282 2.498152 2.320318 0.766005
1 1.197415 3.709942 1.822332 0.344528 2.573235 2.554721 0.741018
2 1.132350 3.984920 1.786031 0.337622 2.670650 2.765547 0.735518
3 1.127946 4.297170 1.868803 0.340183 2.966750 2.708904 0.773274
4 1.146736 4.914509 2.025717 0.343421 3.194934 2.621166 1.008381
.. ... ... ... ... ... ... ...
78 1.094851 5.148720 12.073155 5.450997 2.168452 4.942154 1.410863
79 1.023115 4.319568 12.994531 4.849076 2.147139 4.430529 1.276274
80 0.990413 3.675861 9.969360 4.025533 1.947127 3.965989 1.156245
81 1.027648 3.876272 11.013518 4.427476 2.243652 4.295201 1.170459
82 1.042051 4.734668 10.519551 4.880820 2.605449 4.310781 1.195703
[83 rows x 61 columns]
Data_timeless = Data_aggregated.drop(columns=['year_month'])
print(Data_timeless)
AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM \
0 21.990191 5.123117 1.682904 3.866231 1.419293 1.127155 1.091068
1 22.436759 5.789223 1.694708 3.698123 1.448755 1.106620 1.150508
2 21.613061 6.053151 1.730948 3.574476 1.487183 1.202658 1.209599
3 21.535772 6.349386 1.836533 4.074751 1.667173 1.370815 1.241861
4 22.277677 6.776285 1.959165 4.631329 1.942940 1.535015 1.358492
.. ... ... ... ... ... ... ...
78 19.600504 6.507827 8.201133 2.787143 2.653300 1.688616 3.023953
79 18.934514 6.263428 8.581837 2.438346 2.493987 1.505261 2.933529
80 17.964252 5.769545 8.199104 2.238094 2.717440 1.243186 2.742019
81 19.361205 6.487419 9.108642 2.326004 3.151472 1.146711 2.800497
82 20.240195 7.410547 8.985977 2.393125 3.173809 1.196523 2.822285
ARCLK ASELS ASUZU ... TTKOM TUKAS TUPRS \
0 6.575133 2.997942 2.446388 ... 4.191370 0.954192 28.731524
1 6.661405 3.165903 3.254486 ... 5.483872 0.922549 28.362370
2 7.061526 3.155719 3.982953 ... 5.201549 0.904545 30.145567
3 7.238860 3.610114 4.233941 ... 5.372824 0.930110 33.895264
4 7.907567 3.921994 4.286279 ... 5.784868 0.953549 34.748862
.. ... ... ... ... ... ... ...
78 19.270402 22.756085 7.825997 ... 5.066548 4.134747 131.386789
79 18.108465 21.095927 7.528018 ... 4.406379 3.959702 129.464978
80 16.493457 18.234517 7.159844 ... 4.225021 3.655647 120.396017
81 18.380019 18.156903 6.947476 ... 4.805430 5.031530 119.014340
82 19.732930 17.930801 7.255117 ... 5.328398 4.403008 121.140430
USAK VAKBN VESTL YATAS YKBNK YUNSA ZOREN
0 1.046830 3.710068 1.899769 0.413282 2.498152 2.320318 0.766005
1 1.197415 3.709942 1.822332 0.344528 2.573235 2.554721 0.741018
2 1.132350 3.984920 1.786031 0.337622 2.670650 2.765547 0.735518
3 1.127946 4.297170 1.868803 0.340183 2.966750 2.708904 0.773274
4 1.146736 4.914509 2.025717 0.343421 3.194934 2.621166 1.008381
.. ... ... ... ... ... ... ...
78 1.094851 5.148720 12.073155 5.450997 2.168452 4.942154 1.410863
79 1.023115 4.319568 12.994531 4.849076 2.147139 4.430529 1.276274
80 0.990413 3.675861 9.969360 4.025533 1.947127 3.965989 1.156245
81 1.027648 3.876272 11.013518 4.427476 2.243652 4.295201 1.170459
82 1.042051 4.734668 10.519551 4.880820 2.605449 4.310781 1.195703
[83 rows x 60 columns]
Now we have a data of means of the data for every month
plt.figure(figsize=(16, 6))
plt.boxplot(Data_filled_timeless.iloc[:, :30])
plt.title('Box Plot of the First 30 Columns')
plt.ylabel('Values')
plt.xticks(range(1, 31), Data_filled_timeless.columns[:30], rotation=45)
plt.show()
plt.figure(figsize=(16, 6))
plt.boxplot(Data_filled_timeless.iloc[:, 30:60])
plt.title('Box Plot of the First 30 Columns')
plt.ylabel('Values')
plt.xticks(range(1, 31), Data_filled_timeless.columns[30:60], rotation=45)
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
# Create a scatter plot matrix
scatter_matrix(Data_aggregated.iloc[:, :20], alpha=0.7, figsize=(16, 16), diagonal='scatter', marker='*', s=20)
# Show the plot
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
# Create a scatter plot matrix
scatter_matrix(Data_aggregated.iloc[:, 20:40], alpha=0.7, figsize=(16, 16), diagonal='scatter', marker='*', s=20)
# Show the plot
plt.show()
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
# Create a scatter plot matrix
scatter_matrix(Data_aggregated.iloc[:, 40:60], alpha=0.7, figsize=(16, 16), diagonal='scatter', marker='*', s=20)
# Show the plot
plt.show()
from sklearn.preprocessing import StandardScaler
# Standardizing the features
x = StandardScaler().fit_transform(Data_filled_timeless)
x
array([[ 0.56197627, -1.33921203, -1.98020843, ..., -0.05094032,
-1.34238383, -1.50443231],
[ 0.56197627, -1.35467319, -1.98153131, ..., -0.09157655,
-1.35420384, -1.50443231],
[ 0.56197627, -1.33921203, -1.98020843, ..., -0.09157655,
-1.34637402, -1.50443231],
...,
[-0.20089575, 1.34171073, 0.74633474, ..., 0.45984767,
0.14686271, -0.2469157 ],
[-0.24109346, 1.29935139, 0.75000942, ..., 0.48347338,
0.14686271, -0.2469157 ],
[-0.21697484, 1.29935139, 0.75000942, ..., 0.48347338,
0.14686271, -0.2469157 ]])
from sklearn.decomposition import PCA
import pandas as pd
# Assuming 'data' is your dataset in a DataFrame
# You may need to import your data or load it into a DataFrame first
# Create a PCA instance
pca = PCA()
# Fit the PCA model to your data
pca.fit(x)
# Access the principal components
principal_components = pca.components_
# Access the explained variance ratio
explained_variance_ratio = pca.explained_variance_ratio_
# Access the transformed data in the reduced-dimensional space
reduced_data = pca.transform(x)
principal_components_df = pd.DataFrame(principal_components, columns=Data_filled_timeless.columns)
# Print the explained variance ratios and other results
print("Explained Variance Ratios:", explained_variance_ratio)
print("Principal Components:")
print(principal_components_df)
principal_components_df.describe()
Explained Variance Ratios: [5.08161620e-01 1.72173999e-01 1.07036622e-01 5.26516811e-02
3.77558398e-02 2.22998159e-02 1.48154966e-02 1.05226469e-02
8.76264156e-03 7.91401044e-03 7.80436702e-03 5.90209605e-03
5.31206348e-03 4.78873196e-03 4.07541952e-03 3.08398657e-03
2.85847008e-03 2.35816495e-03 2.26781853e-03 1.84016348e-03
1.62798946e-03 1.39864269e-03 1.23118844e-03 1.14567057e-03
1.06012036e-03 9.56509144e-04 9.25888904e-04 8.28669066e-04
7.58614254e-04 7.11463496e-04 6.41663807e-04 6.25022120e-04
5.44808636e-04 4.91443567e-04 4.74218174e-04 4.10873407e-04
3.67232165e-04 3.31319819e-04 3.03756311e-04 2.78376104e-04
2.42286021e-04 2.33821089e-04 2.17302916e-04 1.90787417e-04
1.81561520e-04 1.76884660e-04 1.59245465e-04 1.39229923e-04
1.26770733e-04 1.22837581e-04 1.02928874e-04 1.01210801e-04
8.81089800e-05 7.97517563e-05 7.32229303e-05 6.76980510e-05
6.15300486e-05 5.28496921e-05 4.44996001e-05 3.83464653e-05]
Principal Components:
AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM \
0 -0.007980 -0.109782 -0.160248 -0.064074 -0.136015 -0.005687 -0.167441
1 -0.163360 -0.215924 -0.037105 -0.161635 -0.086260 -0.113613 0.053168
2 -0.266222 0.073665 0.072071 -0.150605 -0.105502 -0.213452 -0.089453
3 -0.012454 0.088737 -0.081155 0.242470 0.127080 -0.238392 0.090205
4 0.018235 -0.114467 0.065370 0.221813 -0.248909 -0.086165 -0.014644
5 0.013746 0.090637 -0.204370 0.227620 -0.138482 0.236587 0.100457
6 0.046536 0.104690 -0.156258 0.075031 -0.130110 0.356248 0.031835
7 -0.062397 -0.059552 -0.083249 -0.065110 0.002339 0.183951 -0.050151
8 -0.141586 -0.005794 -0.090440 -0.121614 0.127169 0.017123 -0.046635
9 -0.490462 -0.098407 0.103404 0.139423 0.057129 0.455893 0.043175
10 0.252593 0.043546 -0.019556 -0.107833 0.006761 0.044339 -0.042606
11 0.398288 -0.114306 0.229760 -0.015136 -0.040758 0.240643 0.014510
12 0.054940 0.092651 0.013326 0.090902 0.070449 0.221957 0.048859
13 -0.023586 0.071863 0.030764 0.000643 -0.078045 -0.102378 -0.086870
14 0.290620 -0.178166 0.017142 0.168270 0.153440 -0.127168 0.080901
15 -0.174766 -0.066569 -0.058621 0.351734 0.172384 -0.001223 0.074665
16 -0.237653 0.054206 0.151106 -0.017945 0.036810 0.134385 -0.082897
17 -0.119240 0.123907 0.094738 -0.043193 -0.211459 -0.149597 0.055216
18 -0.106737 -0.013408 -0.068518 -0.164402 0.197432 0.048584 0.004462
19 0.086876 -0.013558 0.198012 0.230285 0.074334 -0.184026 0.066233
20 -0.103577 -0.042103 -0.002216 0.164725 -0.030529 -0.230344 -0.050055
21 -0.033171 -0.029061 0.145820 -0.151416 0.141231 0.178515 -0.056544
22 -0.023930 -0.064378 -0.061570 0.088857 -0.080405 0.061280 -0.080529
23 0.089714 -0.084382 -0.128715 0.096356 0.152588 0.140798 0.096297
24 -0.218573 -0.045642 0.016480 0.358492 0.100194 -0.057686 -0.105803
25 -0.078896 -0.034468 -0.268855 -0.004037 -0.106579 -0.016943 -0.185855
26 -0.079512 -0.029437 0.114540 -0.195168 -0.042869 0.060298 -0.113005
27 0.095551 0.049855 -0.117157 0.006708 0.138424 0.126659 -0.075284
28 -0.069165 -0.014021 0.058743 -0.037371 0.153435 -0.116877 0.034848
29 0.099154 0.032333 0.088268 0.034056 -0.043425 0.214867 -0.032360
30 0.092289 0.122357 -0.004112 -0.071244 0.026170 0.020585 -0.071986
31 -0.116272 0.298741 -0.157126 -0.089497 0.197164 -0.018129 -0.003194
32 0.161904 -0.015489 0.046540 0.104489 0.194566 0.059587 0.048334
33 -0.034435 -0.049354 0.226528 -0.001603 0.266981 -0.019731 -0.012422
34 -0.032339 -0.203601 0.074198 -0.037975 0.076530 0.130576 -0.014349
35 -0.043029 -0.150657 -0.152786 -0.081847 0.074188 -0.087437 -0.126986
36 0.094080 0.129498 0.148125 -0.123118 -0.137486 0.025714 -0.095301
37 -0.047891 -0.086916 0.032559 0.153212 -0.190216 0.006308 -0.036765
38 -0.081788 0.119605 0.038598 -0.081180 -0.152540 0.076428 0.055982
39 0.013685 -0.240582 -0.358315 -0.097959 -0.136889 0.012546 0.193490
40 0.073173 0.082066 -0.106478 0.166873 0.034625 0.004663 -0.063419
41 -0.018417 0.106382 0.118922 0.245174 -0.398400 0.031417 0.125245
42 -0.020601 -0.012522 0.106616 -0.034848 -0.107145 -0.029777 0.040650
43 0.009678 -0.046663 -0.081538 -0.140324 -0.090104 -0.023108 0.047884
44 -0.049333 -0.094562 -0.019594 0.108383 -0.037620 0.012545 -0.137163
45 -0.042417 -0.165193 0.223127 -0.104191 -0.225101 0.041299 0.237291
46 0.045367 -0.119475 -0.236521 -0.025069 -0.033796 -0.007031 -0.145130
47 -0.012400 0.445732 -0.021605 -0.010897 -0.022088 -0.037872 0.280470
48 0.043821 -0.246241 -0.088447 0.027792 -0.054947 -0.002329 -0.156569
49 -0.064905 -0.052587 0.006902 -0.087175 0.180114 -0.033752 0.384111
50 -0.076102 -0.041409 0.064562 -0.045402 0.090912 -0.002573 0.208728
51 0.044306 0.293247 -0.068782 0.024161 -0.015038 0.055450 -0.142142
52 -0.003329 -0.236209 0.181565 0.020625 0.006643 0.013881 -0.032606
53 0.003627 -0.065158 0.088379 0.012145 -0.086507 -0.023126 -0.014856
54 -0.030430 -0.027024 0.027586 0.036508 -0.037301 0.026851 0.203007
55 0.011262 -0.137636 -0.095042 -0.110401 -0.007297 -0.023207 0.362293
56 0.022951 0.030469 0.291200 -0.013956 0.080818 -0.004149 -0.280136
57 -0.031590 0.091071 -0.003292 -0.010074 -0.038375 0.000002 -0.054748
58 -0.006653 -0.004936 0.127871 -0.022522 -0.046528 0.007288 0.076089
59 0.018140 0.002061 0.020201 0.085012 0.027467 0.012455 -0.196110
ARCLK ASELS ASUZU ... TTKOM TUKAS TUPRS USAK \
0 -0.121985 -0.174066 -0.148688 ... 0.014811 -0.118634 -0.167450 -0.127121
1 -0.037722 -0.002479 -0.093872 ... -0.267321 0.064886 0.090332 -0.146461
2 0.251883 0.003305 -0.130939 ... 0.109217 0.075216 -0.008250 0.082809
3 0.016899 0.092176 -0.081544 ... -0.116369 -0.123644 0.035795 0.182279
4 -0.139251 0.054779 0.110167 ... -0.051142 -0.278016 -0.067371 0.070142
5 -0.156691 -0.067034 -0.008158 ... 0.011407 0.244813 0.096003 -0.051961
6 0.117616 -0.035796 -0.078856 ... -0.093147 -0.319786 -0.007234 -0.158478
7 -0.027768 0.158099 -0.097405 ... -0.166857 0.000148 0.152530 0.195655
8 0.008805 -0.035204 -0.033747 ... -0.099325 0.029306 -0.040667 -0.041011
9 0.058162 0.010423 0.017911 ... 0.102478 0.120533 0.013823 0.165521
10 0.002190 0.086554 0.158284 ... 0.009098 -0.117089 -0.065234 -0.201846
11 0.146672 -0.027253 -0.070888 ... 0.119636 0.158263 0.045954 0.020608
12 -0.011101 -0.032525 -0.007682 ... -0.042926 0.155310 0.023059 -0.145730
13 -0.021945 -0.059522 -0.038515 ... 0.298005 0.318998 0.068022 0.100003
14 -0.157707 0.018938 0.200411 ... -0.202992 0.176235 0.027405 0.027677
15 -0.050355 0.046369 -0.023416 ... 0.163381 -0.177351 0.001254 -0.129106
16 -0.032519 -0.019776 0.229598 ... -0.312883 -0.076201 -0.019080 0.107280
17 -0.074661 -0.042405 -0.116558 ... 0.150333 -0.119797 -0.084785 0.063964
18 -0.113103 0.060740 0.024416 ... 0.175867 0.015732 0.067978 -0.180499
19 0.053169 -0.121784 0.160763 ... -0.032275 0.120693 -0.071167 0.046497
20 0.012995 0.021370 -0.150004 ... -0.089428 0.225902 0.043832 -0.201883
21 -0.061836 0.040865 -0.005719 ... 0.078856 0.001594 0.010191 0.169478
22 -0.105494 -0.104355 0.060496 ... 0.043259 -0.182544 0.139812 -0.059038
23 -0.025420 0.024902 0.015214 ... 0.332803 0.020109 -0.095889 0.032379
24 -0.005684 0.107308 0.036603 ... -0.181447 0.167523 -0.042994 -0.291118
25 -0.006980 0.097022 0.207756 ... 0.141815 0.198478 0.077229 -0.011382
26 -0.096189 0.028964 -0.120010 ... -0.184632 -0.056557 0.054183 -0.192684
27 0.039289 -0.004979 0.357246 ... 0.020729 -0.133382 -0.095186 0.056569
28 -0.157238 0.088972 0.246306 ... -0.152781 -0.091684 0.055295 -0.045549
29 0.150349 -0.035627 -0.043632 ... -0.314532 0.279212 -0.113390 -0.059879
30 -0.072445 0.097365 -0.228987 ... -0.167041 0.091305 0.088248 -0.004817
31 0.126895 -0.038264 0.014129 ... 0.022399 -0.034654 -0.137838 -0.139486
32 -0.052102 0.003361 -0.368007 ... -0.034183 -0.082714 0.150591 0.031888
33 -0.170432 -0.179605 -0.192678 ... 0.177160 -0.068923 -0.009264 -0.201970
34 -0.026762 -0.172558 0.036741 ... -0.137827 0.026751 -0.062312 -0.054437
35 0.266634 -0.179797 0.187998 ... -0.072940 0.072854 0.080478 0.175964
36 -0.090248 0.063587 0.056241 ... 0.051163 0.084663 -0.028202 -0.115303
37 0.073556 0.060822 -0.041658 ... 0.138248 0.069669 -0.005181 -0.073930
38 -0.368540 0.010280 0.039945 ... -0.089156 0.010563 0.003471 0.205118
39 -0.160937 0.187136 -0.151094 ... -0.074583 0.094693 -0.133336 0.189209
40 0.199512 -0.076169 -0.315304 ... -0.172235 0.011361 -0.363459 0.231488
41 0.026843 -0.008452 0.033991 ... -0.022194 -0.126015 0.140751 -0.066165
42 -0.328206 -0.161748 0.107572 ... 0.033864 0.238222 -0.068057 0.111253
43 0.097695 -0.144801 -0.006187 ... -0.090136 0.014459 0.012503 -0.328712
44 0.219642 -0.058193 0.070539 ... 0.039301 0.005732 0.138703 -0.003097
45 0.233217 -0.064022 0.154013 ... 0.046195 -0.006060 -0.167598 -0.155450
46 0.004729 -0.030258 0.021480 ... 0.062212 0.064609 0.060513 -0.142423
47 0.181951 0.022239 0.059624 ... -0.045789 0.057295 0.124739 -0.006246
48 0.039394 -0.170829 -0.029238 ... 0.010925 -0.145140 0.033763 0.069969
49 0.029587 -0.325030 0.010858 ... -0.037730 -0.046222 -0.032723 0.060118
50 -0.147947 0.166320 -0.077758 ... 0.037176 0.001383 -0.119270 -0.124540
51 -0.128701 0.170248 0.067385 ... -0.007973 0.018115 0.005525 -0.078353
52 0.115073 0.510842 -0.034684 ... 0.069693 -0.018346 -0.270246 -0.058571
53 -0.047663 -0.106092 -0.084357 ... -0.001940 -0.110906 0.174928 0.087482
54 -0.026332 -0.155929 -0.095363 ... -0.009182 0.086903 0.076031 -0.067443
55 0.222440 0.294053 0.056792 ... -0.023089 -0.027065 0.226085 0.029056
56 0.078821 0.163170 -0.060059 ... -0.033767 -0.056967 0.234714 0.100450
57 -0.022305 0.010785 0.039699 ... -0.018095 0.038322 -0.524948 0.029787
58 -0.045541 -0.032359 0.042616 ... -0.028170 -0.053236 -0.133797 0.026259
59 0.055713 -0.181822 -0.004274 ... -0.028624 -0.058733 -0.013943 0.064094
VAKBN VESTL YATAS YKBNK YUNSA ZOREN
0 -0.091523 -0.155937 -0.170246 0.040731 -0.090133 -0.128231
1 -0.252428 0.029923 -0.021708 -0.276577 -0.002581 -0.101419
2 0.020593 0.088437 -0.054690 -0.056988 -0.261302 0.071353
3 0.068797 -0.127410 0.046546 -0.016966 -0.191355 -0.179605
4 -0.109615 -0.004296 0.009768 -0.117349 0.172310 0.177517
5 0.034825 0.131749 -0.000281 0.107032 -0.045935 0.129620
6 0.104957 0.008277 -0.130782 0.079254 0.080813 0.053028
7 -0.030276 -0.242449 -0.047347 -0.152976 -0.116805 -0.125377
8 -0.010873 0.231568 -0.011289 0.170651 -0.072666 0.188148
9 0.056035 -0.015384 -0.018328 -0.125948 -0.027069 0.088380
10 0.034333 0.003343 0.277842 -0.072837 -0.328214 0.198714
11 0.016276 -0.087233 0.044541 -0.103171 -0.031864 -0.334004
12 -0.052718 -0.166360 -0.096745 -0.004224 -0.141290 -0.091752
13 -0.037000 0.053339 -0.027097 0.120543 -0.026765 -0.043181
14 -0.090321 0.098343 -0.119396 -0.047214 0.056648 -0.170199
15 -0.098574 -0.216888 -0.065832 -0.018810 0.018909 0.097933
16 0.096086 0.024216 -0.007343 0.213793 -0.035681 -0.362043
17 0.072109 -0.328973 0.131269 -0.088546 0.149915 -0.220823
18 -0.086051 0.117552 0.129074 0.122633 0.337128 -0.208160
19 0.001348 0.064859 -0.175265 -0.105053 -0.115877 0.223653
20 0.096298 -0.079894 0.024639 -0.058933 0.301662 0.050979
21 -0.107210 -0.096119 0.051227 0.252002 0.176099 0.252600
22 -0.000215 -0.005256 -0.088782 -0.012186 -0.389764 -0.040430
23 -0.165630 -0.032096 0.093337 -0.191345 -0.035555 -0.163388
24 0.144576 -0.022360 0.200601 0.117581 -0.102202 -0.221061
25 0.074675 -0.235003 0.069967 -0.127938 -0.095726 0.096852
26 -0.133178 0.041160 0.114924 -0.205878 0.096481 0.018871
27 0.215783 0.045288 -0.123733 -0.073126 0.280705 -0.068823
28 0.015686 -0.175472 -0.023532 0.101465 0.016646 0.230024
29 -0.060252 -0.286753 0.019128 -0.130230 0.082274 0.201970
30 -0.188815 0.031972 0.001999 0.198570 0.050985 -0.034072
31 -0.018440 0.079025 -0.067852 -0.284261 0.036103 -0.117081
32 0.145882 -0.103288 0.203992 -0.116756 0.076426 0.234923
33 0.168970 -0.002022 0.098905 0.036230 -0.081546 -0.010574
34 0.033978 0.293218 0.264000 -0.136078 -0.114037 -0.014771
35 0.105713 -0.205858 0.199661 -0.040368 0.215593 0.022981
36 0.217448 -0.033608 -0.022585 -0.074371 0.015010 0.032643
37 0.127809 0.361502 -0.083805 -0.253660 0.170848 0.036006
38 0.002290 0.098220 -0.137063 -0.302110 0.025193 0.016161
39 0.271195 0.044461 0.276102 0.132423 -0.090090 0.011019
40 -0.046247 0.134117 -0.173648 0.139177 0.059323 -0.029598
41 -0.017151 0.133301 0.298626 0.116004 0.100549 -0.082334
42 -0.015377 -0.109279 -0.045829 0.117187 -0.025715 0.023530
43 0.240223 -0.106473 -0.151702 0.022297 -0.020851 0.065501
44 -0.470106 0.027777 0.114752 0.061756 -0.038943 0.044469
45 -0.043603 -0.113039 -0.069130 0.230728 -0.039376 -0.033140
46 -0.016175 -0.021679 -0.078828 0.129023 0.048788 0.000246
47 -0.032990 -0.092084 0.140875 -0.058515 -0.003594 0.000128
48 -0.054929 -0.076647 -0.091828 -0.085363 0.015527 -0.039280
49 -0.119311 0.025686 0.140711 -0.069003 -0.045910 0.037151
50 -0.022702 -0.061947 -0.262252 0.014536 -0.012179 -0.022354
51 -0.332079 0.008076 0.040170 0.021009 -0.032186 0.029353
52 -0.078423 -0.044029 0.015873 0.019973 0.003995 0.035962
53 -0.042047 -0.058549 0.022550 0.138436 0.021769 0.016616
54 0.016624 -0.103213 -0.184252 0.014723 0.063236 0.044584
55 0.009089 0.077658 -0.130942 0.006535 -0.020046 0.000706
56 0.216399 0.049881 -0.114774 -0.027399 -0.094796 -0.002292
57 -0.039779 -0.057046 0.154184 -0.047830 -0.052748 0.047829
58 -0.037651 -0.014551 0.149786 -0.054433 0.008412 -0.025205
59 0.008155 0.009114 0.110148 -0.009105 0.026992 0.031712
[60 rows x 60 columns]
| AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ASUZU | ... | TTKOM | TUKAS | TUPRS | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | ... | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 | 60.000000 |
| mean | -0.017411 | -0.013039 | 0.004856 | 0.016549 | -0.005489 | 0.021732 | 0.003240 | -0.004368 | -0.002462 | -0.001908 | ... | -0.015711 | 0.012170 | -0.006585 | -0.015528 | -0.004759 | -0.019486 | 0.010805 | -0.013688 | -0.001241 | -0.000967 |
| std | 0.128999 | 0.129523 | 0.130097 | 0.129115 | 0.130071 | 0.128331 | 0.130148 | 0.130114 | 0.130165 | 0.130175 | ... | 0.129221 | 0.129609 | 0.130019 | 0.129244 | 0.130100 | 0.128697 | 0.129732 | 0.129455 | 0.130183 | 0.130185 |
| min | -0.490462 | -0.246241 | -0.358315 | -0.195168 | -0.398400 | -0.238392 | -0.280136 | -0.368540 | -0.325030 | -0.368007 | ... | -0.314532 | -0.319786 | -0.524948 | -0.328712 | -0.470106 | -0.328973 | -0.262252 | -0.302110 | -0.389764 | -0.362043 |
| 25% | -0.070899 | -0.088827 | -0.081966 | -0.081347 | -0.087407 | -0.024850 | -0.081121 | -0.078558 | -0.064775 | -0.079528 | ... | -0.089605 | -0.070742 | -0.067542 | -0.125185 | -0.056259 | -0.097893 | -0.085049 | -0.092202 | -0.057728 | -0.049591 |
| 50% | -0.019509 | -0.028043 | 0.014903 | -0.010485 | -0.018563 | 0.006798 | -0.014496 | -0.016523 | -0.003729 | -0.004997 | ... | -0.020145 | 0.010962 | 0.004498 | -0.005531 | -0.013125 | -0.009903 | -0.003812 | -0.017888 | -0.007886 | 0.013590 |
| 75% | 0.043943 | 0.072313 | 0.089969 | 0.092266 | 0.083342 | 0.059765 | 0.058544 | 0.062010 | 0.060761 | 0.057500 | ... | 0.047437 | 0.085223 | 0.070025 | 0.070012 | 0.069625 | 0.046437 | 0.111299 | 0.102857 | 0.057316 | 0.051491 |
| max | 0.398288 | 0.445732 | 0.291200 | 0.358492 | 0.266981 | 0.455893 | 0.384111 | 0.266634 | 0.510842 | 0.357246 | ... | 0.332803 | 0.318998 | 0.234714 | 0.231488 | 0.271195 | 0.361502 | 0.298626 | 0.252002 | 0.337128 | 0.252600 |
8 rows × 60 columns
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
, columns = ['principal component 1', 'principal component 2'])
principalDf
| principal component 1 | principal component 2 | |
|---|---|---|
| 0 | 8.395567 | 0.870993 |
| 1 | 8.416420 | 0.953497 |
| 2 | 8.410296 | 0.949745 |
| 3 | 8.406200 | 0.940017 |
| 4 | 8.402837 | 0.911777 |
| ... | ... | ... |
| 50007 | -7.841826 | 2.672890 |
| 50008 | -7.816749 | 2.723456 |
| 50009 | -7.872200 | 2.710949 |
| 50010 | -7.833913 | 2.719308 |
| 50011 | -7.824803 | 2.714428 |
50012 rows × 2 columns
explained_variance_ratio = pca.explained_variance_ratio_
# Print the explained variance ratios
print("Explained Variance Ratios:")
for i, ratio in enumerate(explained_variance_ratio):
print(f"PC{i+1}: {ratio:.2f}")
Explained Variance Ratios: PC1: 0.51 PC2: 0.17
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
, columns = ['principal component 1', 'principal component 2', 'principal component 3'])
principalDf
| principal component 1 | principal component 2 | principal component 3 | |
|---|---|---|---|
| 0 | 8.395567 | 0.870993 | -0.689582 |
| 1 | 8.416420 | 0.953497 | -0.675637 |
| 2 | 8.410296 | 0.949745 | -0.679826 |
| 3 | 8.406200 | 0.940017 | -0.664031 |
| 4 | 8.402837 | 0.911777 | -0.691930 |
| ... | ... | ... | ... |
| 50007 | -7.841826 | 2.672890 | -0.547362 |
| 50008 | -7.816749 | 2.723456 | -0.548091 |
| 50009 | -7.872200 | 2.710949 | -0.532514 |
| 50010 | -7.833913 | 2.719308 | -0.507968 |
| 50011 | -7.824803 | 2.714428 | -0.508086 |
50012 rows × 3 columns
explained_variance_ratio = pca.explained_variance_ratio_
# Print the explained variance ratios
print("Explained Variance Ratios:")
for i, ratio in enumerate(explained_variance_ratio):
print(f"PC{i+1}: {ratio:.2f}")
Explained Variance Ratios: PC1: 0.51 PC2: 0.17 PC3: 0.11
print("Standard deviations:")
print(pca.explained_variance_)
print("Proportion of variance:")
print(pca.explained_variance_ratio_)
print("Cumulative proportion of variance:")
print(pca.explained_variance_ratio_.cumsum())
print("Loadings:")
print(pca.components_)
Standard deviations: [30.49030684 10.33064651 6.42232574] Proportion of variance: [0.50816162 0.172174 0.10703662] Cumulative proportion of variance: [0.50816162 0.68033562 0.78737224] Loadings: [[-0.00797989 -0.10978214 -0.16024786 -0.06407439 -0.13601486 -0.00568723 -0.16744146 -0.1219848 -0.17406645 -0.14868821 -0.16719171 0.07737154 -0.16272716 -0.06192938 0.09492331 -0.11766653 -0.16685591 -0.17442011 -0.17324891 -0.13112733 -0.10217255 0.02856573 0.10830698 -0.14620672 -0.12042445 -0.14350589 -0.11668073 -0.15096929 -0.16926912 -0.1161842 -0.16794952 -0.04518889 -0.1469359 -0.03664199 -0.12375029 -0.15469878 -0.17518554 -0.05711586 -0.01416825 -0.05712187 -0.16617377 -0.16868442 0.08926098 -0.15675589 -0.15538192 -0.14878012 -0.15117475 -0.1508164 -0.17598265 -0.07712832 0.01481144 -0.1186344 -0.16744994 -0.12712093 -0.09152314 -0.15593672 -0.17024646 0.04073147 -0.0901334 -0.12823051] [-0.16336015 -0.21592426 -0.03710458 -0.16163536 -0.08625967 -0.11361338 0.05316789 -0.03772152 -0.00247925 -0.09387236 -0.03551955 -0.1300294 -0.04932394 -0.06152575 -0.14837222 -0.05333797 -0.04310382 0.05520566 0.06291216 -0.18088806 -0.06317509 -0.11195898 -0.21521401 0.04692607 -0.19823917 0.11969054 0.15139875 0.10904285 -0.00182617 -0.10602862 0.00682269 -0.0674117 0.01287499 -0.28509068 -0.0170458 0.0751868 0.0030477 -0.03245286 -0.19218273 -0.26437786 0.08129119 0.08857294 -0.18322757 0.13393748 0.00644959 0.05001733 0.12450551 -0.05137538 0.01650922 -0.25742241 -0.2673212 0.06488638 0.09033229 -0.14646071 -0.25242815 0.02992342 -0.02170823 -0.2765768 -0.00258073 -0.1014195 ] [-0.26622225 0.07366462 0.0720708 -0.1506053 -0.10550232 -0.2134519 -0.08945294 0.25188258 0.00330487 -0.13093946 0.09497906 0.13403878 -0.02498091 0.19929729 -0.13989354 -0.07683641 0.08004287 -0.0298757 -0.03178404 0.01294246 0.17870569 0.25830419 -0.08283132 -0.07808297 0.02321468 -0.15428514 -0.06003885 0.03558096 -0.07846125 -0.03480374 0.11356639 -0.27965583 -0.14241383 0.03006372 0.24270922 0.01337871 0.04251661 -0.25970513 -0.20422314 0.05284977 -0.09045705 0.02285603 -0.13930962 0.04485308 -0.00492186 -0.16064703 -0.08783478 0.17616002 -0.041297 0.08453179 0.10921686 0.07521602 -0.00824983 0.08280865 0.02059318 0.0884369 -0.05468989 -0.05698797 -0.26130164 0.07135254]]
Certainly, here's a concise summary of PC1, PC2, and PC3 based on their explained variance ratios in a Principal Component Analysis (PCA):
PC1:
Explained Variance: 51% Dominant latent variable capturing the primary data pattern. PC2:
Explained Variance: 17% Captures additional, uncorrelated patterns. PC3:
Explained Variance: 11% Represents independent, less prominent patterns.
from sklearn.decomposition import PCA
pca = PCA(n_components=9)
principalComponents = pca.fit_transform(x)
principalDf = pd.DataFrame(data = principalComponents
, columns = ['principal component 1', 'principal component 2','principal component 3','principal component 4','principal component 5','principal component 6','principal component 7','principal component 8''principal component 9','principal component 10'])
principalDf
explained_variance_ratio = pca.explained_variance_ratio_
# Print the explained variance ratios
print("Explained Variance Ratios:")
for i, ratio in enumerate(explained_variance_ratio):
print(f"PC{i+1}: {ratio:.2f}")
Explained Variance Ratios: PC1: 0.51 PC2: 0.17 PC3: 0.11 PC4: 0.05 PC5: 0.04 PC6: 0.02 PC7: 0.01 PC8: 0.01 PC9: 0.01
import matplotlib.pyplot as plt
# Principal component labels (PC1 to PC9)
components = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7', 'PC8', 'PC9']
# Explained variances for PC1 to PC9
explained_variances = [0.51, 0.17, 0.11, 0.05, 0.04, 0.02, 0.01, 0.01, 0.01]
# Create a bar plot
plt.bar(components, explained_variances, color='skyblue')
plt.xlabel('Principal Components')
plt.ylabel('Explained Variance')
plt.title('Explained Variance for Principal Components (PC1 to PC9)')
plt.show()
correlation_matrix = Data_filled_timeless.corr()
np.fill_diagonal(correlation_matrix.values, 0)
# Display the correlation matrix
print(correlation_matrix)
AEFES AKBNK AKSA AKSEN ALARK ALBRK ANACM \
AEFES 0.000000 0.265172 -0.012122 0.521308 0.315361 0.503105 0.106100
AKBNK 0.265172 0.000000 0.567416 0.531172 0.656149 0.160338 0.438333
AKSA -0.012122 0.567416 0.000000 0.219147 0.626336 -0.063763 0.707960
AKSEN 0.521308 0.531172 0.219147 0.000000 0.446445 0.285215 0.438764
ALARK 0.315361 0.656149 0.626336 0.446445 0.000000 0.158141 0.734758
ALBRK 0.503105 0.160338 -0.063763 0.285215 0.158141 0.000000 0.068882
ANACM 0.106100 0.438333 0.707960 0.438764 0.734758 0.068882 0.000000
ARCLK -0.337666 0.643148 0.740921 -0.038953 0.461082 -0.253037 0.448305
ASELS 0.035064 0.583076 0.844850 0.408688 0.747129 -0.063239 0.894125
ASUZU 0.440741 0.584673 0.754438 0.566683 0.718186 0.318852 0.752457
AYGAZ -0.051980 0.677050 0.899645 0.291432 0.696477 -0.104259 0.775535
BAGFS 0.006692 0.033987 -0.274151 0.039251 -0.416179 -0.046661 -0.531851
BANVT 0.153372 0.659102 0.772829 0.471309 0.861936 -0.056583 0.834471
BRISA -0.229100 0.297074 0.486006 -0.176182 -0.054764 0.067195 0.060022
CCOLA 0.481101 -0.081474 -0.410763 -0.078869 -0.124391 0.494149 -0.555986
CEMAS 0.220141 0.459101 0.578476 0.659717 0.531732 0.001944 0.667902
ECILC -0.040566 0.685352 0.893949 0.379831 0.722899 -0.139906 0.789561
EREGL 0.015220 0.424460 0.831668 0.296478 0.653305 -0.022930 0.929395
FROTO 0.005818 0.415052 0.818819 0.265335 0.650406 0.002325 0.925018
GARAN 0.326121 0.936442 0.628744 0.498170 0.724072 0.258061 0.576693
GOODY -0.152160 0.535968 0.570250 0.126226 0.282307 -0.042670 0.351869
GUBRF -0.222250 0.132776 0.161095 -0.204825 -0.395999 -0.085913 -0.433420
HALKB 0.437222 0.116712 -0.540029 0.251099 -0.155746 0.382223 -0.597118
ICBCT 0.106240 0.328913 0.654832 0.293869 0.544015 0.036152 0.783822
ISCTR 0.300368 0.909821 0.639640 0.497510 0.751942 0.204917 0.519111
ISDMR 0.098281 0.150550 0.570717 0.273853 0.534514 0.144348 0.893909
ISFIN -0.118504 0.083453 0.397492 -0.064247 0.411083 0.149047 0.736413
ISYAT -0.201476 0.301473 0.728234 -0.113542 0.577324 -0.076428 0.780396
KAREL 0.165144 0.563324 0.779169 0.439729 0.809521 0.119894 0.936724
KARSN 0.269095 0.492122 0.669882 0.381247 0.384358 0.351149 0.504051
KCHOL -0.149012 0.621032 0.839680 0.164146 0.653170 -0.063910 0.797950
KRDMB 0.543139 0.061509 0.282523 0.226658 0.455579 0.477097 0.252868
KRDMD 0.286676 0.329243 0.685679 0.448415 0.517678 0.202323 0.799012
MGROS 0.412346 0.765185 0.281262 0.594540 0.399468 0.202422 0.032288
OTKAR -0.369788 0.572501 0.745584 -0.076631 0.449921 -0.227990 0.479687
PARSN -0.101860 0.365266 0.746728 0.271369 0.471572 -0.118050 0.825768
PETKM -0.022398 0.618495 0.863999 0.339058 0.723767 -0.070263 0.884443
PGSUS 0.455231 0.112036 0.248354 0.086575 0.558672 0.442826 0.344626
PRKME 0.621465 0.426034 -0.024117 0.706689 0.482905 0.332525 0.144670
SAHOL 0.420153 0.874562 0.359967 0.458704 0.491823 0.280905 0.137065
SASA 0.058802 0.348357 0.718169 0.343454 0.652378 0.034038 0.963333
SISE -0.145425 0.389975 0.767653 0.136784 0.611452 -0.016399 0.908831
SKBNK 0.530356 -0.040103 -0.349426 0.079131 -0.186915 0.545840 -0.563067
SODA -0.266101 0.253605 0.708470 0.029746 0.497584 -0.131516 0.857113
TCELL 0.076181 0.451006 0.821456 0.031484 0.612873 0.158314 0.719892
THYAO 0.236087 0.255787 0.673597 0.363101 0.549559 0.270220 0.850862
TKFEN -0.024584 0.258495 0.555781 0.189139 0.633321 0.044481 0.928476
TOASO -0.171724 0.670973 0.897573 0.206580 0.581100 -0.233571 0.622719
TRKCM 0.082097 0.502678 0.854274 0.361278 0.726391 0.050399 0.923831
TSKB 0.331086 0.855472 0.530657 0.574207 0.423720 0.177319 0.207461
TTKOM 0.256178 0.577971 0.126886 0.207921 0.078597 0.235238 -0.308916
TUKAS -0.225400 0.312425 0.579092 -0.109250 0.483431 -0.007662 0.594107
TUPRS -0.106395 0.382141 0.732139 0.205825 0.652578 -0.029318 0.928222
USAK 0.068725 0.784005 0.724250 0.560589 0.650606 -0.089070 0.558428
VAKBN 0.396090 0.942750 0.503089 0.594608 0.651004 0.302873 0.344008
VESTL -0.157526 0.481660 0.780727 0.090731 0.505632 -0.033687 0.740200
YATAS 0.194608 0.597285 0.822706 0.424558 0.782436 0.051352 0.889055
YKBNK 0.540599 0.516445 -0.178244 0.385192 0.158014 0.443267 -0.320544
YUNSA 0.457522 0.084808 0.403309 0.374625 0.379516 0.454777 0.555568
ZOREN 0.054961 0.625503 0.698531 0.337452 0.388649 0.176522 0.516512
ARCLK ASELS ASUZU ... TTKOM TUKAS TUPRS \
AEFES -0.337666 0.035064 0.440741 ... 0.256178 -0.225400 -0.106395
AKBNK 0.643148 0.583076 0.584673 ... 0.577971 0.312425 0.382141
AKSA 0.740921 0.844850 0.754438 ... 0.126886 0.579092 0.732139
AKSEN -0.038953 0.408688 0.566683 ... 0.207921 -0.109250 0.205825
ALARK 0.461082 0.747129 0.718186 ... 0.078597 0.483431 0.652578
ALBRK -0.253037 -0.063239 0.318852 ... 0.235238 -0.007662 -0.029318
ANACM 0.448305 0.894125 0.752457 ... -0.308916 0.594107 0.928222
ARCLK 0.000000 0.644469 0.325279 ... 0.235208 0.543088 0.571272
ASELS 0.644469 0.000000 0.779530 ... -0.125700 0.536585 0.893284
ASUZU 0.325279 0.779530 0.000000 ... 0.110716 0.384536 0.636442
AYGAZ 0.835521 0.929536 0.688782 ... 0.044636 0.524998 0.814792
BAGFS -0.146777 -0.410044 -0.309174 ... 0.453582 -0.256471 -0.513158
BANVT 0.586699 0.936172 0.780917 ... -0.026286 0.499072 0.809699
BRISA 0.492417 0.247454 0.284722 ... 0.375983 0.358573 0.205034
CCOLA -0.396623 -0.562904 -0.192156 ... 0.407348 -0.467859 -0.634347
CEMAS 0.292184 0.699250 0.659773 ... -0.065199 0.075078 0.506580
ECILC 0.801955 0.940326 0.704750 ... 0.052609 0.498830 0.797005
EREGL 0.564238 0.923790 0.773014 ... -0.256906 0.600595 0.929223
FROTO 0.553140 0.907939 0.757108 ... -0.254112 0.651169 0.943424
GARAN 0.585888 0.670255 0.720528 ... 0.444973 0.463186 0.534531
GOODY 0.676808 0.558910 0.378788 ... 0.187098 0.287757 0.440353
GUBRF 0.267541 -0.186179 -0.124541 ... 0.611728 -0.001442 -0.293304
HALKB -0.429528 -0.566417 -0.274574 ... 0.531831 -0.547289 -0.698433
ICBCT 0.370746 0.837018 0.677726 ... -0.296821 0.401830 0.811565
ISCTR 0.601024 0.608979 0.664213 ... 0.514421 0.433461 0.461925
ISDMR 0.207117 0.721796 0.663487 ... -0.473105 0.565494 0.864175
ISFIN 0.316428 0.555274 0.380215 ... -0.455299 0.689811 0.801579
ISYAT 0.632613 0.730029 0.538174 ... -0.275088 0.793710 0.873391
KAREL 0.510225 0.895765 0.816987 ... -0.119497 0.661234 0.883856
KARSN 0.276207 0.569759 0.813804 ... 0.286294 0.397713 0.455717
KCHOL 0.831227 0.876829 0.633989 ... -0.015850 0.730062 0.886452
KRDMB -0.167014 0.213080 0.540296 ... 0.053014 -0.046850 0.110626
KRDMD 0.253266 0.758343 0.837855 ... -0.190449 0.333910 0.717417
MGROS 0.276749 0.248521 0.389868 ... 0.739777 -0.094708 -0.061236
OTKAR 0.919723 0.633919 0.371461 ... 0.190012 0.596799 0.589513
PARSN 0.545087 0.830431 0.633380 ... -0.299878 0.478594 0.830680
PETKM 0.732799 0.962750 0.742724 ... -0.091351 0.606537 0.891425
PGSUS -0.144599 0.225062 0.509287 ... -0.021835 0.298204 0.261100
PRKME -0.225790 0.165693 0.347944 ... 0.319377 -0.156251 -0.031074
SAHOL 0.458573 0.276917 0.423083 ... 0.762486 0.086096 0.055916
SASA 0.431820 0.878004 0.736117 ... -0.371578 0.596543 0.935729
SISE 0.650024 0.879453 0.630035 ... -0.308587 0.709901 0.962126
SKBNK -0.492411 -0.491880 -0.034939 ... 0.489633 -0.451638 -0.642820
SODA 0.611731 0.816731 0.514626 ... -0.418108 0.707969 0.950503
TCELL 0.593385 0.783634 0.738172 ... -0.016310 0.657050 0.780132
THYAO 0.210616 0.767963 0.818661 ... -0.279923 0.479639 0.792847
TKFEN 0.379961 0.779138 0.587129 ... -0.473376 0.688869 0.936478
TOASO 0.895946 0.839020 0.598904 ... 0.186133 0.502404 0.685348
TRKCM 0.587642 0.942208 0.823648 ... -0.166879 0.548554 0.891752
TSKB 0.491454 0.426481 0.558325 ... 0.737732 0.085888 0.127556
TTKOM 0.235208 -0.125700 0.110716 ... 0.000000 -0.054951 -0.340949
TUKAS 0.543088 0.536585 0.384536 ... -0.054951 0.000000 0.737531
TUPRS 0.571272 0.893284 0.636442 ... -0.340949 0.737531 0.000000
USAK 0.649184 0.759339 0.613717 ... 0.335591 0.334332 0.535926
VAKBN 0.521497 0.489618 0.596321 ... 0.647514 0.190639 0.251842
VESTL 0.679081 0.751010 0.656484 ... -0.041763 0.724395 0.795190
YATAS 0.542902 0.932127 0.846644 ... -0.047544 0.564226 0.844456
YKBNK -0.122521 -0.260872 0.106371 ... 0.752921 -0.267913 -0.446585
YUNSA -0.137644 0.425622 0.697668 ... -0.156237 0.138050 0.415343
ZOREN 0.546037 0.637184 0.715896 ... 0.323633 0.388265 0.499454
USAK VAKBN VESTL YATAS YKBNK YUNSA ZOREN
AEFES 0.068725 0.396090 -0.157526 0.194608 0.540599 0.457522 0.054961
AKBNK 0.784005 0.942750 0.481660 0.597285 0.516445 0.084808 0.625503
AKSA 0.724250 0.503089 0.780727 0.822706 -0.178244 0.403309 0.698531
AKSEN 0.560589 0.594608 0.090731 0.424558 0.385192 0.374625 0.337452
ALARK 0.650606 0.651004 0.505632 0.782436 0.158014 0.379516 0.388649
ALBRK -0.089070 0.302873 -0.033687 0.051352 0.443267 0.454777 0.176522
ANACM 0.558428 0.344008 0.740200 0.889055 -0.320544 0.555568 0.516512
ARCLK 0.649184 0.521497 0.679081 0.542902 -0.122521 -0.137644 0.546037
ASELS 0.759339 0.489618 0.751010 0.932127 -0.260872 0.425622 0.637184
ASUZU 0.613717 0.596321 0.656484 0.846644 0.106371 0.697668 0.715896
AYGAZ 0.807072 0.584630 0.763965 0.841454 -0.180368 0.269624 0.660128
BAGFS -0.008295 0.072626 -0.273651 -0.404082 0.408554 -0.369039 -0.008490
BANVT 0.797426 0.587242 0.670332 0.901837 -0.075558 0.366467 0.566885
BRISA 0.263538 0.238441 0.544262 0.182941 0.017194 0.128996 0.667101
CCOLA -0.350226 0.108308 -0.547587 -0.492072 0.617757 0.073131 -0.293610
CEMAS 0.633222 0.432485 0.421761 0.662592 -0.052363 0.470942 0.476323
ECILC 0.845233 0.599778 0.752808 0.858607 -0.160646 0.284822 0.662617
EREGL 0.580790 0.320863 0.844860 0.894513 -0.366473 0.575305 0.622709
FROTO 0.534652 0.296154 0.824209 0.888916 -0.374623 0.564103 0.614078
GARAN 0.734838 0.888731 0.592027 0.717789 0.415514 0.278288 0.683128
GOODY 0.552288 0.448244 0.594462 0.443432 -0.005511 0.032096 0.628992
GUBRF 0.118210 0.123091 0.024583 -0.256328 0.183377 -0.274285 0.274241
HALKB -0.094000 0.287207 -0.669084 -0.538088 0.796595 -0.188620 -0.321035
ICBCT 0.511308 0.241439 0.580014 0.776574 -0.352774 0.553379 0.489382
ISCTR 0.784523 0.915621 0.538133 0.647523 0.465811 0.254137 0.584164
ISDMR 0.279140 0.063307 0.669293 0.757176 -0.426444 0.677550 0.393291
ISFIN 0.105329 -0.001316 0.634780 0.593841 -0.485735 0.357839 0.269007
ISYAT 0.329222 0.158897 0.811276 0.721688 -0.448969 0.355421 0.475290
KAREL 0.654569 0.501534 0.773698 0.936903 -0.152536 0.522391 0.589410
KARSN 0.490048 0.487213 0.617787 0.647264 0.124203 0.569324 0.781845
KCHOL 0.660173 0.480493 0.868622 0.799409 -0.238772 0.247141 0.667008
KRDMB 0.082963 0.220459 0.060000 0.332500 0.191926 0.708839 0.158768
KRDMD 0.444986 0.296528 0.690673 0.791991 -0.179659 0.801137 0.630788
MGROS 0.695834 0.839489 0.048836 0.237457 0.722927 0.046474 0.399510
OTKAR 0.549266 0.443693 0.726979 0.549746 -0.178876 -0.062797 0.582825
PARSN 0.515861 0.215951 0.749318 0.766841 -0.428126 0.442551 0.556136
PETKM 0.726206 0.500513 0.811259 0.917050 -0.266180 0.346237 0.679104
PGSUS 0.032829 0.172792 0.176820 0.365953 0.169164 0.603661 0.155074
PRKME 0.412514 0.543268 -0.207194 0.250312 0.566828 0.197844 0.041971
SAHOL 0.562702 0.896935 0.217775 0.320650 0.734075 0.047056 0.459516
SASA 0.508513 0.249574 0.749880 0.870744 -0.405306 0.597570 0.509971
SISE 0.502559 0.259367 0.843284 0.813822 -0.456254 0.424449 0.573767
SKBNK -0.242786 0.158943 -0.512879 -0.377077 0.648642 0.072636 -0.102745
SODA 0.426467 0.104101 0.805562 0.723136 -0.583118 0.369757 0.471818
TCELL 0.453374 0.379217 0.757042 0.793327 -0.211518 0.520254 0.645894
THYAO 0.365013 0.207472 0.646612 0.805468 -0.309348 0.773839 0.574430
TKFEN 0.327262 0.127966 0.719675 0.774942 -0.450968 0.483484 0.384817
TOASO 0.788273 0.568148 0.763305 0.751614 -0.139907 0.116256 0.685480
TRKCM 0.635641 0.427903 0.815065 0.913053 -0.272866 0.602336 0.667245
TSKB 0.728469 0.880002 0.340900 0.459221 0.573489 0.091424 0.664517
TTKOM 0.335591 0.647514 -0.041763 -0.047544 0.752921 -0.156237 0.323633
TUKAS 0.334332 0.190639 0.724395 0.564226 -0.267913 0.138050 0.388265
TUPRS 0.535926 0.251842 0.795190 0.844456 -0.446585 0.415343 0.499454
USAK 0.000000 0.752704 0.495247 0.675461 0.165511 0.137390 0.565870
VAKBN 0.752704 0.000000 0.350838 0.531143 0.634297 0.141870 0.561643
VESTL 0.495247 0.350838 0.000000 0.754297 -0.230744 0.358992 0.759059
YATAS 0.675461 0.531143 0.754297 0.000000 -0.147035 0.502024 0.652366
YKBNK 0.165511 0.634297 -0.230744 -0.147035 0.000000 -0.025209 0.106437
YUNSA 0.137390 0.141870 0.358992 0.502024 -0.025209 0.000000 0.379015
ZOREN 0.565870 0.561643 0.759059 0.652366 0.106437 0.379015 0.000000
[60 rows x 60 columns]
stacked_correlations = correlation_matrix.stack()
# Sort the pairs by correlation value in descending order
sorted_correlations = stacked_correlations.sort_values(ascending=False)
# Get the pair(s) with the highest correlation
highest_correlation_pairs = sorted_correlations.head(6)
# Display the pair(s) with the highest correlation
print("Pair(s) with the highest correlation:")
print(highest_correlation_pairs)
Pair(s) with the highest correlation: TRKCM EREGL 0.974073 EREGL TRKCM 0.974073 FROTO EREGL 0.972648 EREGL FROTO 0.972648 AYGAZ ECILC 0.971154 ECILC AYGAZ 0.971154 dtype: float64
plt.plot(Data_filled_timeless['TRKCM'], label='List 1')
plt.plot(Data_filled_timeless['EREGL'], label='List 2')
#a= StandardScaler().fit_transform(Data_filled_timeless['TRKCM'])
# Adding labels and legend
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot of Two Lists')
plt.legend()
# Display the plot
plt.show()
plt.plot(Data_filled_timeless['AYGAZ'], label='List 1')
plt.plot(Data_filled_timeless['ECILC'], label='List 2')
# Adding labels and legend
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot of Two Lists')
plt.legend()
# Display the plot
plt.show()
plt.plot(Data_filled_timeless['OTKAR'], label='List 1')
plt.plot(Data_filled_timeless['TUPRS'], label='List 2')
# Adding labels and legend
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot of Two Lists')
plt.legend()
# Display the plot
plt.show()
I choose these 3 pairs to analyze it 2 of them for their correlation rate and one of them that i liked it from the boxplot analysis
# Create a DataFrame with a 'Month' column ranging from 1 to 87
DataPair1 = pd.DataFrame({'Month': list(range(1, 82))})
DataPair2 = pd.DataFrame({'Month': list(range(1, 82))})
DataPair3 = pd.DataFrame({'Month': list(range(1, 82))})
DataPair1['OTKAR'] = Data_timeless['OTKAR']
DataPair1['TUPRS'] = Data_timeless['TUPRS']
DataPair2['AYGAZ'] = Data_timeless['AYGAZ']
DataPair2['ECILC'] = Data_timeless['ECILC']
DataPair3['TRKCM'] = Data_timeless['TRKCM']
DataPair3['EREGL'] = Data_timeless['EREGL']
# Display the DataFrame
print(DataPair1)
output = pd.DataFrame(DataPair1)
output
Month OTKAR TUPRS 0 1 25.196903 28.731524 1 2 25.656762 28.362370 2 3 25.318374 30.145567 3 4 25.770522 33.895264 4 5 27.400952 34.748862 .. ... ... ... 76 77 96.105671 112.551039 77 78 108.700879 127.260837 78 79 112.274200 131.386789 79 80 107.906259 129.464978 80 81 103.328378 120.396017 [81 rows x 3 columns]
| Month | OTKAR | TUPRS | |
|---|---|---|---|
| 0 | 1 | 25.196903 | 28.731524 |
| 1 | 2 | 25.656762 | 28.362370 |
| 2 | 3 | 25.318374 | 30.145567 |
| 3 | 4 | 25.770522 | 33.895264 |
| 4 | 5 | 27.400952 | 34.748862 |
| ... | ... | ... | ... |
| 76 | 77 | 96.105671 | 112.551039 |
| 77 | 78 | 108.700879 | 127.260837 |
| 78 | 79 | 112.274200 | 131.386789 |
| 79 | 80 | 107.906259 | 129.464978 |
| 80 | 81 | 103.328378 | 120.396017 |
81 rows × 3 columns
output2 = pd.DataFrame(DataPair2)
print(output2)
output3 = pd.DataFrame(DataPair3)
print(output3)
list_corr1 = []
list_corr2 = []
list_corr3 = []
Month AYGAZ ECILC
0 1 3.454684 0.810134
1 2 3.440262 0.788359
2 3 3.692111 0.782510
3 4 4.045729 0.824110
4 5 4.391287 0.886488
.. ... ... ...
76 77 10.378431 2.494400
77 78 10.157537 2.702138
78 79 10.057255 2.654632
79 80 9.484292 2.473291
80 81 8.746060 2.313946
[81 rows x 3 columns]
Month TRKCM EREGL
0 1 0.429993 0.786624
1 2 0.417833 0.755184
2 3 0.440100 0.758716
3 4 0.464104 0.840054
4 5 0.565576 0.896085
.. ... ... ...
76 77 3.028934 6.610235
77 78 3.482505 7.370253
78 79 3.568281 7.687748
79 80 3.124604 7.802562
80 81 2.828925 7.417945
[81 rows x 3 columns]
output['OTKAR'].rolling(6).corr(output['TUPRS'])
# formatting the output
k = 1
for i, j in enumerate(output['OTKAR'].rolling(6).corr(output['TUPRS'])):
if (i >= 5 and i < 82):
print(f'The correlation in stocks during months\
{k} through {i+1} is {j}')
list_corr1.append(round(j,2))
i = 0
k += 1
The correlation in stocks during months 1 through 6 is 0.7883928312390968 The correlation in stocks during months 2 through 7 is 0.7165450123326561 The correlation in stocks during months 3 through 8 is 0.3147359150633959 The correlation in stocks during months 4 through 9 is 0.17920008545138466 The correlation in stocks during months 5 through 10 is -0.4765864350639622 The correlation in stocks during months 6 through 11 is -0.4874653402396 The correlation in stocks during months 7 through 12 is -0.4010560326076644 The correlation in stocks during months 8 through 13 is 0.19613465512679554 The correlation in stocks during months 9 through 14 is 0.8577770541272218 The correlation in stocks during months 10 through 15 is 0.897679056857705 The correlation in stocks during months 11 through 16 is 0.6470827243098127 The correlation in stocks during months 12 through 17 is 0.8792959699052186 The correlation in stocks during months 13 through 18 is 0.9391989890319423 The correlation in stocks during months 14 through 19 is 0.8775706135960225 The correlation in stocks during months 15 through 20 is 0.5726803598869044 The correlation in stocks during months 16 through 21 is 0.5052478737531151 The correlation in stocks during months 17 through 22 is 0.9315006421220431 The correlation in stocks during months 18 through 23 is 0.9905932451337914 The correlation in stocks during months 19 through 24 is 0.9814350020540931 The correlation in stocks during months 20 through 25 is 0.7961189586849352 The correlation in stocks during months 21 through 26 is -0.6564870128758725 The correlation in stocks during months 22 through 27 is -0.4488511199053289 The correlation in stocks during months 23 through 28 is 0.45300775988262304 The correlation in stocks during months 24 through 29 is 0.8334805228285507 The correlation in stocks during months 25 through 30 is 0.9043764816849587 The correlation in stocks during months 26 through 31 is 0.9258520705087581 The correlation in stocks during months 27 through 32 is 0.7999678358604003 The correlation in stocks during months 28 through 33 is 0.5990352660409132 The correlation in stocks during months 29 through 34 is -0.1555726240012433 The correlation in stocks during months 30 through 35 is -0.3647114214536127 The correlation in stocks during months 31 through 36 is -0.6630856726562067 The correlation in stocks during months 32 through 37 is -0.9385767630101385 The correlation in stocks during months 33 through 38 is -0.8072816755329058 The correlation in stocks during months 34 through 39 is -0.48782326988127817 The correlation in stocks during months 35 through 40 is -0.2527393687185909 The correlation in stocks during months 36 through 41 is -0.049896241863486776 The correlation in stocks during months 37 through 42 is -0.35841180629266506 The correlation in stocks during months 38 through 43 is 0.05059179569227542 The correlation in stocks during months 39 through 44 is 0.7433299124924181 The correlation in stocks during months 40 through 45 is 0.9186031742436659 The correlation in stocks during months 41 through 46 is 0.697895065945761 The correlation in stocks during months 42 through 47 is 0.48161774502290844 The correlation in stocks during months 43 through 48 is 0.2876363337947782 The correlation in stocks during months 44 through 49 is 0.19595159158857126 The correlation in stocks during months 45 through 50 is -0.9316549638320035 The correlation in stocks during months 46 through 51 is 0.2129789866568973 The correlation in stocks during months 47 through 52 is 0.8634627421344687 The correlation in stocks during months 48 through 53 is 0.9767789167126626 The correlation in stocks during months 49 through 54 is 0.9665812900831784 The correlation in stocks during months 50 through 55 is 0.8117064839344403 The correlation in stocks during months 51 through 56 is 0.5482650353178739 The correlation in stocks during months 52 through 57 is 0.23961668017496876 The correlation in stocks during months 53 through 58 is -0.6330862023871466 The correlation in stocks during months 54 through 59 is -0.674632565674954 The correlation in stocks during months 55 through 60 is -0.6204113581522965 The correlation in stocks during months 56 through 61 is -0.7996274084342989 The correlation in stocks during months 57 through 62 is -0.8991215948797153 The correlation in stocks during months 58 through 63 is -0.5751057043285953 The correlation in stocks during months 59 through 64 is -0.5942282982850526 The correlation in stocks during months 60 through 65 is -0.47239496838801337 The correlation in stocks during months 61 through 66 is -0.5186991773608818 The correlation in stocks during months 62 through 67 is -0.5934851927239764 The correlation in stocks during months 63 through 68 is -0.359838479748605 The correlation in stocks during months 64 through 69 is 0.11126004163289537 The correlation in stocks during months 65 through 70 is -0.31983989161815624 The correlation in stocks during months 66 through 71 is -0.2705612337394221 The correlation in stocks during months 67 through 72 is -0.23899518686556664 The correlation in stocks during months 68 through 73 is -0.15732931325405244 The correlation in stocks during months 69 through 74 is 0.2755588913711922 The correlation in stocks during months 70 through 75 is 0.5690782357211369 The correlation in stocks during months 71 through 76 is 0.38518048106333386 The correlation in stocks during months 72 through 77 is 0.19105100562017588 The correlation in stocks during months 73 through 78 is 0.48476346509145785 The correlation in stocks during months 74 through 79 is 0.7287865889376727 The correlation in stocks during months 75 through 80 is 0.92481931417226 The correlation in stocks during months 76 through 81 is 0.9488076742666673
output2['AYGAZ'].rolling(3).corr(output2['ECILC'])
# formatting the output
k = 1
for i, j in enumerate(output2['AYGAZ'].rolling(3).corr(output2['ECILC'])):
if (i >=2 and i < 82):
print(f'The correlation in stocks during months\
{k} through {i+1} is {j}')
list_corr2.append(round(j,2))
i = 0
k += 1
The correlation in stocks during months 1 through 3 is -0.6247924019064495 The correlation in stocks during months 2 through 4 is 0.8487970518193222 The correlation in stocks during months 3 through 5 is 0.9926255884117215 The correlation in stocks during months 4 through 6 is 0.6354404943528326 The correlation in stocks during months 5 through 7 is 0.6642472411033291 The correlation in stocks during months 6 through 8 is 0.9679230543069464 The correlation in stocks during months 7 through 9 is 0.8783443602937578 The correlation in stocks during months 8 through 10 is 0.1338504988036819 The correlation in stocks during months 9 through 11 is 0.6653633614091338 The correlation in stocks during months 10 through 12 is -0.7166750456711233 The correlation in stocks during months 11 through 13 is -0.9991454088387202 The correlation in stocks during months 12 through 14 is 0.5665858181940739 The correlation in stocks during months 13 through 15 is 0.9907940370161482 The correlation in stocks during months 14 through 16 is 0.9494656674540457 The correlation in stocks during months 15 through 17 is 0.9997507202573741 The correlation in stocks during months 16 through 18 is 0.7239193464004651 The correlation in stocks during months 17 through 19 is -0.9905956966758789 The correlation in stocks during months 18 through 20 is 0.8501861280072003 The correlation in stocks during months 19 through 21 is 0.8867740992917723 The correlation in stocks during months 20 through 22 is 0.9153146302210713 The correlation in stocks during months 21 through 23 is 0.35519574068932125 The correlation in stocks during months 22 through 24 is -0.9991014844026582 The correlation in stocks during months 23 through 25 is -0.985187736324145 The correlation in stocks during months 24 through 26 is -0.9991311474177981 The correlation in stocks during months 25 through 27 is 0.7471354905485799 The correlation in stocks during months 26 through 28 is 0.9895917117419468 The correlation in stocks during months 27 through 29 is 0.9745630914161906 The correlation in stocks during months 28 through 30 is 0.7218359412277003 The correlation in stocks during months 29 through 31 is 0.9372703673743512 The correlation in stocks during months 30 through 32 is 0.9548347282574001 The correlation in stocks during months 31 through 33 is 0.9741115017947843 The correlation in stocks during months 32 through 34 is 0.7124607301021518 The correlation in stocks during months 33 through 35 is -0.8124095241331774 The correlation in stocks during months 34 through 36 is 0.20387223565897666 The correlation in stocks during months 35 through 37 is 0.9054678044443778 The correlation in stocks during months 36 through 38 is 0.887448324279588 The correlation in stocks during months 37 through 39 is 0.9999769436689041 The correlation in stocks during months 38 through 40 is 0.8559255808091821 The correlation in stocks during months 39 through 41 is -0.15381002177047878 The correlation in stocks during months 40 through 42 is 0.6044147218090721 The correlation in stocks during months 41 through 43 is 0.9557465097169345 The correlation in stocks during months 42 through 44 is 0.977317116585786 The correlation in stocks during months 43 through 45 is 0.9598474641476087 The correlation in stocks during months 44 through 46 is 0.40856533836711467 The correlation in stocks during months 45 through 47 is 0.6134448266424374 The correlation in stocks during months 46 through 48 is 0.9532460695958852 The correlation in stocks during months 47 through 49 is -0.8534252385154951 The correlation in stocks during months 48 through 50 is -0.6053175902694018 The correlation in stocks during months 49 through 51 is 0.9937120418622475 The correlation in stocks during months 50 through 52 is -0.6773168261470577 The correlation in stocks during months 51 through 53 is 0.8745111033982509 The correlation in stocks during months 52 through 54 is 0.9911583939677056 The correlation in stocks during months 53 through 55 is 0.934333400486254 The correlation in stocks during months 54 through 56 is 0.905245669835003 The correlation in stocks during months 55 through 57 is 0.9999564974468999 The correlation in stocks during months 56 through 58 is 0.9590778162909225 The correlation in stocks during months 57 through 59 is 0.9108240095787367 The correlation in stocks during months 58 through 60 is -0.42211067584003126 The correlation in stocks during months 59 through 61 is 0.8330964349184018 The correlation in stocks during months 60 through 62 is 0.8987306751891881 The correlation in stocks during months 61 through 63 is -0.1662726032034934 The correlation in stocks during months 62 through 64 is 0.5539975817435749 The correlation in stocks during months 63 through 65 is 0.861301202841466 The correlation in stocks during months 64 through 66 is 0.9723974040830231 The correlation in stocks during months 65 through 67 is 0.960290576247184 The correlation in stocks during months 66 through 68 is -0.8609250925289245 The correlation in stocks during months 67 through 69 is 0.9906339388604045 The correlation in stocks during months 68 through 70 is 0.9743729411999114 The correlation in stocks during months 69 through 71 is 0.9166223972591643 The correlation in stocks during months 70 through 72 is 0.3919491836641618 The correlation in stocks during months 71 through 73 is 0.9199855833120772 The correlation in stocks during months 72 through 74 is -0.33731393270997423 The correlation in stocks during months 73 through 75 is 0.7114367806342057 The correlation in stocks during months 74 through 76 is 0.997432861201181 The correlation in stocks during months 75 through 77 is 0.20551719844902983 The correlation in stocks during months 76 through 78 is -0.6592066402463481 The correlation in stocks during months 77 through 79 is -0.8627657766020904 The correlation in stocks during months 78 through 80 is 0.9982314389785114 The correlation in stocks during months 79 through 81 is 0.9939667794607046
output3['TRKCM'].rolling(3).corr(output3['EREGL'])
# formatting the output
k = 1
for i, j in enumerate(output3['TRKCM'].rolling(3).corr(output3['EREGL'])):
if (i >= 2 and i < 82):
print(f'The correlation in stocks during months\
{k} through {i+1} is {j}')
list_corr3.append(round(j,2))
i = 0
k += 1
The correlation in stocks during months 1 through 3 is 0.1552909426729977 The correlation in stocks during months 2 through 4 is 0.8937595275974255 The correlation in stocks during months 3 through 5 is 0.9011544792478664 The correlation in stocks during months 4 through 6 is 0.6384148794819002 The correlation in stocks during months 5 through 7 is 0.6858388606406023 The correlation in stocks during months 6 through 8 is -0.0664047267957055 The correlation in stocks during months 7 through 9 is 0.9996916014794439 The correlation in stocks during months 8 through 10 is 0.9889142971376731 The correlation in stocks during months 9 through 11 is 0.5049618124526561 The correlation in stocks during months 10 through 12 is -0.4365335698765683 The correlation in stocks during months 11 through 13 is -0.9665081732869942 The correlation in stocks during months 12 through 14 is -0.21276165694812274 The correlation in stocks during months 13 through 15 is 0.9464272626676075 The correlation in stocks during months 14 through 16 is 0.13902439607023864 The correlation in stocks during months 15 through 17 is -0.6817157668639549 The correlation in stocks during months 16 through 18 is -0.48278246546478404 The correlation in stocks during months 17 through 19 is 0.9931532820078057 The correlation in stocks during months 18 through 20 is 0.9678075776250548 The correlation in stocks during months 19 through 21 is 0.9898717297406795 The correlation in stocks during months 20 through 22 is 0.9612926195168585 The correlation in stocks during months 21 through 23 is 0.9916863593244125 The correlation in stocks during months 22 through 24 is 0.999742385453099 The correlation in stocks during months 23 through 25 is -0.9223145890880804 The correlation in stocks during months 24 through 26 is -0.037488717282529754 The correlation in stocks during months 25 through 27 is 0.8966121026418151 The correlation in stocks during months 26 through 28 is 0.47335443683622125 The correlation in stocks during months 27 through 29 is 0.6743730100302326 The correlation in stocks during months 28 through 30 is -0.0410941751552051 The correlation in stocks during months 29 through 31 is 0.759014095887258 The correlation in stocks during months 30 through 32 is 0.4252740096579751 The correlation in stocks during months 31 through 33 is 0.9510919467600029 The correlation in stocks during months 32 through 34 is -0.5062955557001445 The correlation in stocks during months 33 through 35 is 0.7521172857527338 The correlation in stocks during months 34 through 36 is 0.9955578380750912 The correlation in stocks during months 35 through 37 is 0.9941841562866262 The correlation in stocks during months 36 through 38 is 0.9572339481043544 The correlation in stocks during months 37 through 39 is 0.9339878869705567 The correlation in stocks during months 38 through 40 is 0.9989035074649002 The correlation in stocks during months 39 through 41 is 0.9802034333390601 The correlation in stocks during months 40 through 42 is 0.9281499749837885 The correlation in stocks during months 41 through 43 is 0.9982354324972812 The correlation in stocks during months 42 through 44 is 0.9984252473528794 The correlation in stocks during months 43 through 45 is 0.9225147616507794 The correlation in stocks during months 44 through 46 is -0.9628562409183636 The correlation in stocks during months 45 through 47 is 0.8124577064772022 The correlation in stocks during months 46 through 48 is 0.9971294393737137 The correlation in stocks during months 47 through 49 is 0.6248325673468231 The correlation in stocks during months 48 through 50 is 0.16809785830512736 The correlation in stocks during months 49 through 51 is -0.8526747020048057 The correlation in stocks during months 50 through 52 is 0.9890645191383636 The correlation in stocks during months 51 through 53 is 0.9988643845653128 The correlation in stocks during months 52 through 54 is 0.9999768332072027 The correlation in stocks during months 53 through 55 is 0.9825620721615638 The correlation in stocks during months 54 through 56 is 0.9260469611969653 The correlation in stocks during months 55 through 57 is 0.9822385683378518 The correlation in stocks during months 56 through 58 is 0.9406243077241488 The correlation in stocks during months 57 through 59 is 0.9514711840925445 The correlation in stocks during months 58 through 60 is 0.9924878764574232 The correlation in stocks during months 59 through 61 is 0.5014234406775986 The correlation in stocks during months 60 through 62 is 0.9999042700167743 The correlation in stocks during months 61 through 63 is 0.9760796065209538 The correlation in stocks during months 62 through 64 is 0.9854938494042039 The correlation in stocks during months 63 through 65 is 0.9884052859929707 The correlation in stocks during months 64 through 66 is 0.9330901527024322 The correlation in stocks during months 65 through 67 is 0.6418322690141343 The correlation in stocks during months 66 through 68 is 0.8836151181268307 The correlation in stocks during months 67 through 69 is -0.9992289416796162 The correlation in stocks during months 68 through 70 is -0.9981223457508694 The correlation in stocks during months 69 through 71 is 0.7541074809730113 The correlation in stocks during months 70 through 72 is 0.7979833189888588 The correlation in stocks during months 71 through 73 is 0.6664608259733191 The correlation in stocks during months 72 through 74 is 0.8676481383283822 The correlation in stocks during months 73 through 75 is 0.9894107044141528 The correlation in stocks during months 74 through 76 is 0.9729893102570754 The correlation in stocks during months 75 through 77 is 0.6032840080025191 The correlation in stocks during months 76 through 78 is 0.9998194621531629 The correlation in stocks during months 77 through 79 is 0.9898980085817916 The correlation in stocks during months 78 through 80 is -0.5643007991728114 The correlation in stocks during months 79 through 81 is 0.5948888827190905
print(list_corr1)
print(list_corr2)
print(list_corr3)
[0.79, 0.72, 0.31, 0.18, -0.48, -0.49, -0.4, 0.2, 0.86, 0.9, 0.65, 0.88, 0.94, 0.88, 0.57, 0.51, 0.93, 0.99, 0.98, 0.8, -0.66, -0.45, 0.45, 0.83, 0.9, 0.93, 0.8, 0.6, -0.16, -0.36, -0.66, -0.94, -0.81, -0.49, -0.25, -0.05, -0.36, 0.05, 0.74, 0.92, 0.7, 0.48, 0.29, 0.2, -0.93, 0.21, 0.86, 0.98, 0.97, 0.81, 0.55, 0.24, -0.63, -0.67, -0.62, -0.8, -0.9, -0.58, -0.59, -0.47, -0.52, -0.59, -0.36, 0.11, -0.32, -0.27, -0.24, -0.16, 0.28, 0.57, 0.39, 0.19, 0.48, 0.73, 0.92, 0.95] [-0.62, 0.85, 0.99, 0.64, 0.66, 0.97, 0.88, 0.13, 0.67, -0.72, -1.0, 0.57, 0.99, 0.95, 1.0, 0.72, -0.99, 0.85, 0.89, 0.92, 0.36, -1.0, -0.99, -1.0, 0.75, 0.99, 0.97, 0.72, 0.94, 0.95, 0.97, 0.71, -0.81, 0.2, 0.91, 0.89, 1.0, 0.86, -0.15, 0.6, 0.96, 0.98, 0.96, 0.41, 0.61, 0.95, -0.85, -0.61, 0.99, -0.68, 0.87, 0.99, 0.93, 0.91, 1.0, 0.96, 0.91, -0.42, 0.83, 0.9, -0.17, 0.55, 0.86, 0.97, 0.96, -0.86, 0.99, 0.97, 0.92, 0.39, 0.92, -0.34, 0.71, 1.0, 0.21, -0.66, -0.86, 1.0, 0.99] [0.16, 0.89, 0.9, 0.64, 0.69, -0.07, 1.0, 0.99, 0.5, -0.44, -0.97, -0.21, 0.95, 0.14, -0.68, -0.48, 0.99, 0.97, 0.99, 0.96, 0.99, 1.0, -0.92, -0.04, 0.9, 0.47, 0.67, -0.04, 0.76, 0.43, 0.95, -0.51, 0.75, 1.0, 0.99, 0.96, 0.93, 1.0, 0.98, 0.93, 1.0, 1.0, 0.92, -0.96, 0.81, 1.0, 0.62, 0.17, -0.85, 0.99, 1.0, 1.0, 0.98, 0.93, 0.98, 0.94, 0.95, 0.99, 0.5, 1.0, 0.98, 0.99, 0.99, 0.93, 0.64, 0.88, -1.0, -1.0, 0.75, 0.8, 0.67, 0.87, 0.99, 0.97, 0.6, 1.0, 0.99, -0.56, 0.59]
import seaborn as sns
correlation_values = list_corr1
# Create a list of time period labels
time_periods = list(range(1, 77))
# Create a data frame for the correlations
correlation_data = pd.DataFrame({'Time Period': time_periods, 'Correlation': list_corr1})
# Create a heatmap using seaborn
plt.figure(figsize=(30, 20))
sns.heatmap(data=correlation_data.pivot_table(index='Time Period', columns='Time Period', values='Correlation'),
annot=True, fmt=".3f", cmap="coolwarm", xticklabels=True, yticklabels=True)
plt.title('Correlations in OTKAR TUPRAS Over Different Time Periods')
plt.show()
import seaborn as sns
correlation_values = list_corr2
# Create a list of time period labels
time_periods = list(range(1, 80))
# Create a data frame for the correlations
correlation_data = pd.DataFrame({'Time Period': time_periods, 'Correlation': list_corr2})
# Create a heatmap using seaborn
plt.figure(figsize=(30, 20))
sns.heatmap(data=correlation_data.pivot_table(index='Time Period', columns='Time Period', values='Correlation'),
annot=True, fmt=".3f", cmap="coolwarm", xticklabels=True, yticklabels=True)
plt.title('Correlations in AYGAZ ECILC Over Different Time Periods')
plt.show()
import seaborn as sns
correlation_values = list_corr3
# Create a list of time period labels
time_periods = list(range(1, 80))
# Create a data frame for the correlations
correlation_data = pd.DataFrame({'Time Period': time_periods, 'Correlation': list_corr3})
# Create a heatmap using seaborn
plt.figure(figsize=(30, 20))
sns.heatmap(data=correlation_data.pivot_table(index='Time Period', columns='Time Period', values='Correlation'),
annot=True, fmt=".3f", cmap="coolwarm", xticklabels=True, yticklabels=True)
plt.title('Correlations in TRKCM EREGL Over Different Time Periods')
plt.show()
As we can see in the graph in 3 month window correlation last 2 stock pairs does look like correlated. But the first one even in 6 months window correlation is not correlated.
TRKCM AND EREGL even though they are not ın same ındustry they have a tendency to grow together
aygaz and ecılc are also have a sımılıar tendencyç
However otkar and tuprs have a seasonal varıabılıty sometımes correlated ın a posıtıve way sometımes correlated ın a negatıve way
Google_tupras = pd.read_csv (r'C:\Users\erdil/Desktop/DataMining/tupras.csv')
print(Google_tupras)
point = Google_tupras['Tupras'].tolist()
Ay Tupras 0 2012-09 39 1 2012-10 32 2 2012-11 32 3 2012-12 29 4 2013-01 29 .. ... ... 73 2018-10 69 74 2018-11 63 75 2018-12 43 76 2019-01 45 77 2019-02 65 [78 rows x 2 columns]
my_list = Data_timeless['TUPRS']
Tupras_monthly= my_list[:-5]
Tupras_monthly.head(79)
0 28.731524
1 28.362370
2 30.145567
3 33.895264
4 34.748862
...
73 120.739631
74 111.781556
75 108.765277
76 112.551039
77 127.260837
Name: TUPRS, Length: 78, dtype: float64
correlation = np.corrcoef(point, Tupras_monthly)[0, 1]
# Print the correlation coefficient
print("Correlation coefficient:", correlation)
Correlation coefficient: 0.6482784191509634
plt.plot(point, label='List 1')
plt.plot(Tupras_monthly, label='List 2')
# Adding labels and legend
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot of Two Lists')
plt.legend()
# Display the plot
plt.show()
from sklearn.preprocessing import StandardScaler
mean = np.mean(point)
std_dev = np.std(point)
# Standardize the list
standardized_data = [(x - mean) / std_dev for x in point]
print("Original Data:", point)
print("Standardized Data:", standardized_data)
Original Data: [39, 32, 32, 29, 29, 57, 40, 44, 37, 31, 62, 51, 38, 31, 28, 29, 34, 28, 36, 30, 28, 29, 31, 25, 28, 28, 50, 63, 37, 37, 37, 36, 30, 34, 38, 44, 41, 30, 35, 31, 31, 56, 38, 41, 34, 37, 38, 44, 35, 37, 28, 28, 29, 49, 53, 37, 36, 38, 36, 44, 34, 100, 64, 56, 49, 60, 61, 55, 62, 46, 55, 78, 69, 69, 63, 43, 45, 65] Standardized Data: [-0.22835059487528367, -0.7270682940829033, -0.7270682940829033, -0.9408044508861689, -0.9408044508861689, 1.0540663459443098, -0.15710520927419516, 0.12787633313015895, -0.3708413660774607, -0.7983136796839918, 1.4102932739497522, 0.6265940323377786, -0.2995959804763722, -0.7983136796839918, -1.0120498364872574, -0.9408044508861689, -0.5845775228807263, -1.0120498364872574, -0.4420867516785492, -0.8695590652850804, -1.0120498364872574, -0.9408044508861689, -0.7983136796839918, -1.225785993290523, -1.0120498364872574, -1.0120498364872574, 0.55534864673669, 1.4815386595508409, -0.3708413660774607, -0.3708413660774607, -0.3708413660774607, -0.4420867516785492, -0.8695590652850804, -0.5845775228807263, -0.2995959804763722, 0.12787633313015895, -0.08585982367310663, -0.8695590652850804, -0.5133321372796378, -0.7983136796839918, -0.7983136796839918, 0.9828209603432212, -0.2995959804763722, -0.08585982367310663, -0.5845775228807263, -0.3708413660774607, -0.2995959804763722, 0.12787633313015895, -0.5133321372796378, -0.3708413660774607, -1.0120498364872574, -1.0120498364872574, -0.9408044508861689, 0.48410326113560154, 0.7690848035399557, -0.3708413660774607, -0.4420867516785492, -0.2995959804763722, -0.4420867516785492, 0.12787633313015895, -0.5845775228807263, 4.117617926791116, 1.5527840451519295, 0.9828209603432212, 0.48410326113560154, 1.2678025027475752, 1.3390478883486638, 0.9115755747421327, 1.4102932739497522, 0.27036710433233596, 0.9115755747421327, 2.5502194435671686, 1.909010973157372, 1.909010973157372, 1.4815386595508409, 0.05663094752907041, 0.19912171873124745, 1.6240294307530179]
from sklearn.preprocessing import StandardScaler
mean = np.mean(Tupras_monthly)
std_dev = np.std(Tupras_monthly)
# Standardize the list
standardized_data2 = [(x - mean) / std_dev for x in Tupras_monthly]
print("Original Data:", Tupras_monthly)
print("Standardized Data:", standardized_data2)
Original Data: 0 28.731524
1 28.362370
2 30.145567
3 33.895264
4 34.748862
...
73 120.739631
74 111.781556
75 108.765277
76 112.551039
77 127.260837
Name: TUPRS, Length: 78, dtype: float64
Standardized Data: [-0.9810877619858778, -0.9939574817924203, -0.9317903237080088, -0.8010655871985596, -0.7713067889682206, -0.8115824007927293, -0.7235502594758607, -0.7980915650445255, -0.7666474471743273, -0.9200272160005454, -0.9470068589297677, -0.9887761791362074, -0.9829855382212704, -0.94294063525415, -0.9398366400675167, -0.9381574128091521, -1.0257569656489671, -1.0500746515129193, -0.9839035114103037, -0.8867449797582589, -0.8330709716050739, -0.7770771526778124, -0.7825659931676247, -0.7880525366198537, -0.8213739330451089, -0.9001979225120124, -0.8097290279829, -0.7489695427365224, -0.6764472943312632, -0.7098281747531779, -0.6851146211237416, -0.5118332496575076, -0.44971618982200423, -0.4350074879669723, -0.33399795677822314, -0.2740272430378818, -0.27302193311098516, -0.18726113445080497, -0.19506026391591005, -0.29195610005563283, -0.24378646074415306, -0.273617622860055, -0.15026325824181141, -0.07833072129459302, -0.2522005545533431, -0.3419701200800962, -0.3551354015778867, -0.44083695816619534, -0.49926284352075523, -0.42208126401772905, -0.3053521256533427, -0.17018184544846748, -0.05058332173510784, 0.1911664481263599, 0.300966731993504, 0.3750741325584217, 0.6036377195347973, 0.7501175563408639, 0.9182401158832605, 1.1957443296882995, 1.370795471753283, 1.5566915075036796, 1.5837160033253224, 1.304174656422991, 1.3094085437298546, 1.16549223190505, 1.2859616159192624, 1.442940355999981, 1.197806035449306, 1.4525478709703694, 1.278473795317726, 1.5749753299841607, 2.0503230862797057, 2.226567903316353, 1.9142647987936612, 1.8091090142374384, 1.9410910945236692, 2.45391508257682]
plt.plot(standardized_data, label='search')
plt.plot(standardized_data2, label='stock')
# Adding labels and legend
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot tupras monthly stock prices and search points of Two Lists')
plt.legend()
# Display the plot
plt.show()
we can see that in the search area, the change on the stock market looks like affected the searching on google about the company
dogalgaz = pd.read_csv (r'C:\Users\erdil/Desktop/DataMining/dogalgaz.csv')
print(dogalgaz)
point2 = dogalgaz['dogalgaz'].tolist()
point2
Ay dogalgaz 0 2012-09 48 1 2012-10 39 2 2012-11 48 3 2012-12 48 4 2013-01 50 .. ... ... 73 2018-10 82 74 2018-11 92 75 2018-12 97 76 2019-01 100 77 2019-02 80 [78 rows x 2 columns]
[48, 39, 48, 48, 50, 35, 33, 30, 29, 27, 30, 31, 40, 42, 44, 66, 40, 36, 29, 32, 30, 31, 32, 32, 40, 46, 58, 55, 65, 40, 39, 37, 31, 33, 33, 38, 39, 70, 67, 82, 74, 52, 43, 43, 42, 36, 32, 37, 49, 69, 75, 86, 80, 85, 58, 51, 51, 45, 52, 48, 63, 84, 86, 81, 73, 69, 61, 56, 52, 50, 65, 63, 74, 82, 92, 97, 100, 80]
plt.plot(point2, label='List 1')
plt.plot(point, label='List 2')
# Adding labels and legend
plt.title('Line Plot of tupras and dogalgaz search Two Lists')
plt.legend()
# Display the plot
plt.show()
plt.plot(point2, label='List 1')
plt.plot(point, label='List 2')
# Adding labels and legend
plt.title('Line Plot of tupras and dogalgaz search Two Lists')
plt.legend()
# Display the plot
plt.show()
my_list = Data_timeless['AYGAZ']
AYGAZ= my_list[:-5]
AYGAZ.head(79)
0 3.454684
1 3.440262
2 3.692111
3 4.045729
4 4.391287
...
73 11.001865
74 10.356621
75 10.166670
76 10.378431
77 10.157537
Name: AYGAZ, Length: 78, dtype: float64
plt.plot(point2, label='List 1')
plt.plot(AYGAZ, label='List 2')
# Adding labels and legend
plt.title('Line Plot of tupras and AYGAZ search Two Lists')
plt.legend()
# Display the plot
plt.show()
from sklearn.preprocessing import StandardScaler
mean = np.mean(AYGAZ)
std_dev = np.std(AYGAZ)
# Standardize the list
standardized_data2 = [(x - mean) / std_dev for x in AYGAZ]
print("Original Data:", AYGAZ)
print("Standardized Data:", standardized_data2)
Original Data: 0 3.454684
1 3.440262
2 3.692111
3 4.045729
4 4.391287
...
73 11.001865
74 10.356621
75 10.166670
76 10.378431
77 10.157537
Name: AYGAZ, Length: 78, dtype: float64
Standardized Data: [-1.6251116341051677, -1.6305473133480493, -1.535623584792904, -1.4023422536212469, -1.2720990451498697, -1.332059703283792, -1.2047610512374702, -1.150321939725467, -0.5571422305905849, -0.6229099164077705, -0.7963980077632569, -0.8347241445714896, -0.8806390667182825, -0.7370300087385203, -0.8580988475147934, -0.9350539832192772, -0.9634874336628002, -0.9812677346991174, -0.9542463701736354, -0.8745053533788223, -0.8012774412150925, -0.7616474018459362, -0.7674469367738501, -0.6133290120285887, -0.675833091488109, -0.7300554557758279, -0.6084068932053405, -0.5762174702137112, -0.4772977503672464, -0.5451170873931576, -0.6977883993557807, -0.5894714865742811, -0.4850008213238434, -0.4287759495348224, -0.3724819500078027, -0.4082279813549125, -0.5349767838397097, -0.38579747667299197, -0.23662258451434962, -0.413160384459494, -0.5207970057523074, -0.34945032577287655, -0.09836273505150339, 0.17750017981819927, 0.04900006615448127, -0.008993014570297646, 0.07298733924039878, 0.14149914046494944, -0.050039660807416265, 0.01557186975774071, -0.0017603095863625365, 0.10272591014982325, 0.31522912587645857, 0.6767088449969305, 0.940509322900795, 1.2236036088588156, 1.6134835348894905, 1.8309864283676829, 1.7957606573028766, 1.7208387404239558, 1.7173886902585915, 1.6924218475607433, 1.6680184570351526, 1.5812894300265312, 1.8481086817016086, 1.6196559700645397, 1.6466047365286234, 1.7102539606315248, 1.0248928604205079, 0.7033963632591304, 0.5436984814499731, 0.7065704798489846, 1.1618158939019412, 1.2194765791004005, 0.9762794403183661, 0.9046854517391602, 0.9844997511745581, 0.9012431879649893]
from sklearn.preprocessing import StandardScaler
mean = np.mean(point2)
std_dev = np.std(point2)
# Standardize the list
standardized_data2 = [(x - mean) / std_dev for x in point2]
print("Original Data:", point2)
print("Standardized Data:", standardized_data2)
Original Data: [48, 39, 48, 48, 50, 35, 33, 30, 29, 27, 30, 31, 40, 42, 44, 66, 40, 36, 29, 32, 30, 31, 32, 32, 40, 46, 58, 55, 65, 40, 39, 37, 31, 33, 33, 38, 39, 70, 67, 82, 74, 52, 43, 43, 42, 36, 32, 37, 49, 69, 75, 86, 80, 85, 58, 51, 51, 45, 52, 48, 63, 84, 86, 81, 73, 69, 61, 56, 52, 50, 65, 63, 74, 82, 92, 97, 100, 80] Standardized Data: [-0.2448277074499042, -0.7131357473132088, -0.2448277074499042, -0.2448277074499042, -0.14075925414694765, -0.9212726539191219, -1.0253411072220786, -1.1814437871765133, -1.2334780138279917, -1.3375464671309483, -1.1814437871765133, -1.1294095605250352, -0.6611015206617306, -0.5570330673587739, -0.45296461405581734, 0.691788372276705, -0.6611015206617306, -0.8692384272676437, -1.2334780138279917, -1.0773753338735568, -1.1814437871765133, -1.1294095605250352, -1.0773753338735568, -1.0773753338735568, -0.6611015206617306, -0.3488961607528608, 0.27551455906487865, 0.1194118791104438, 0.6397541456252267, -0.6611015206617306, -0.7131357473132088, -0.8172042006161654, -1.1294095605250352, -1.0253411072220786, -1.0253411072220786, -0.7651699739646871, -0.7131357473132088, 0.8999252788826181, 0.7438225989281833, 1.5243359987003575, 1.1080621854885313, -0.03669080084399106, -0.5049988407072956, -0.5049988407072956, -0.5570330673587739, -0.8692384272676437, -1.0773753338735568, -0.8172042006161654, -0.19279348079842593, 0.8478910522311398, 1.1600964121400095, 1.7324729053062706, 1.420267545397401, 1.6804386786547925, 0.27551455906487865, -0.08872502749546934, -0.08872502749546934, -0.40093038740433906, -0.03669080084399106, -0.2448277074499042, 0.5356856923222701, 1.628404452003314, 1.7324729053062706, 1.4723017720488794, 1.056027958837053, 0.8478910522311398, 0.43161723901931354, 0.1714461057619221, -0.03669080084399106, -0.14075925414694765, 0.6397541456252267, 0.5356856923222701, 1.1080621854885313, 1.5243359987003575, 2.0446782652151403, 2.304849398472532, 2.460952078426967, 1.420267545397401]
plt.plot(standardized_data, label='List 1')
plt.plot(standardized_data2, label='List 2')
# Adding labels and legend
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot of dogalgaz and AYGAZ search Two Lists')
plt.legend()
# Display the plot
plt.show()
we can see that after standartizing the dataö the search volume and the seasonalıty also affected the stock prıces about AYGAZ. On the wınter tımes hıgher trend was to search for dogalgazç